A compression management system for post-natal abdominal incision healing in pregnant women

Abstract

The application relates to the technical field of pressure management, and provides a pressure management system for healing of a postpartum abdominal incision of a pregnant woman, wherein a pressure sensor is used for real-time monitoring of pressure applied to the abdominal incision and transmitting a detected pressure signal to a frequency domain signal analysis module; the frequency domain signal analysis module extracts high-frequency and low-frequency signal components by performing spectrum analysis on the pressure signal and transmits the data to a state recognition module; the state recognition module judges the physiological state of the pregnant woman according to spectrum information and a pre-trained model; a pressure regulation module dynamically adjusts the applied pressure value according to the recognized state information and the frequency domain signal data; the regulation process is optimized in real time through a feedback regulation optimization unit, the unit continuously collects the gap between the current pressure data and the target pressure, and finely adjusts the pressure value; and the application can better adapt to the needs under different physiological states through dynamic pressure adjustment and real-time response.

Description

Technical Field

[0001] This invention relates to the field of pressure management technology, and more specifically, to a pressure management system for postpartum abdominal incision healing in pregnant women. Background Technology

[0002] The healing process of abdominal incisions is crucial for pregnant women after surgeries such as cesarean sections. Due to the loose tissues and thicker fat layers around the incision, blood supply is relatively weak, making complications such as fat liquefaction, infection, and wound dehiscence more likely. These complications not only prolong healing time but can also affect the patient's overall recovery. To address this issue, pressure healing devices have been widely used in recent years for the recovery of abdominal incisions after cesarean sections. These devices apply moderate external pressure to help promote blood and lymphatic drainage in the wound area, thereby reducing swelling, fluid accumulation, and the risk of infection, and accelerating the wound healing process. Pressure healing devices have become an important tool in postpartum incision management, especially when it is necessary to reduce incision tension and control exudate.

[0003] The efficacy of pressure therapy in abdominal incision healing has been validated by multiple studies. For example, a systematic review and meta-analysis in published literature 1 (Application of traditional Chinese medicine for fat liquefaction in abdominal incision: A systematic review and meta-analysis. Traditional Journal of Natural Products Research, 2017, 1(4), 137-152.) showed that external application of traditional Chinese medicine combined with pressure therapy can significantly reduce the incidence of postoperative fat liquefaction, especially in the healing process of abdominal incisions. This study, through a systematic review of various traditional Chinese medicine pressure therapies, demonstrated the positive role of pressure in reducing inflammation and accelerating healing, and also showed significant advantages in preventing incision infection. This indicates that the effect of pressure therapy in controlling abdominal incision complications and promoting healing is clear, further supporting the application prospects of pressure management systems in this field.

[0004] Among existing technologies, the Mott compression garment is one of the more common compression devices for postpartum abdominal incisions. Reference 2 (Use of Mott compression garment in cesarean delivery patients. Obstetrics & Gynecology, 2001, 97(4), 622-627.) investigated its application in patients after cesarean section. The study showed that the Mott compression garment can provide stable external pressure, reduce postoperative pain, help reduce wound swelling, and prevent wound dehiscence. However, this device has poor flexibility in responding to changes in patient position and cannot dynamically adjust the pressure value according to different patient states, affecting its effectiveness in different activity states. Although the Mott compression garment has certain effects, there is still room for improvement in the responsiveness and personalized adjustment of existing compression devices.

[0005] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, this invention provides a pressure management system for postpartum abdominal incision healing in pregnant women. Through the coordinated operation of a pressure sensor, a frequency domain signal analysis module, a status recognition module, and a pressure control module, the system dynamically adjusts the pressure value at the incision site and responds precisely according to the patient's physiological state, effectively solving the problem that existing pressure devices cannot flexibly adjust the pressure when the patient's position changes.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A pressure management system for postpartum abdominal incision healing in pregnant women includes a pressure sensor, a frequency domain signal analysis module, a state recognition module, and a pressure regulation module. The pressure sensor is connected to the frequency domain signal analysis module, which is connected to the state recognition module, and the state recognition module is connected to the pressure regulation module. The state recognition module identifies the current physiological state of the pregnant woman, including resting state and postural change state, based on the spectral information output by the frequency domain signal analysis module and a pre-trained classification model. When the identification result is a resting state, a stable regulation mode is entered; when the identification result is a postural change state, a rapid response mode is entered.

[0009] The pressure regulation module dynamically adjusts the pressure applied to the abdominal incision of the pregnant woman based on the status information output by the status recognition module and the spectrum data provided by the frequency domain signal analysis module, and optimizes the regulation process through a real-time feedback mechanism. It includes a stabilization regulation unit, a dynamic signal trend evaluation unit, a signal response regulation unit, and a feedback adjustment optimization unit. The stabilization regulation unit adjusts the pressure value in a stable regulation mode when the status recognition module detects that the pregnant woman is in a resting state. The dynamic signal trend evaluation unit executes a rapid response mode to analyze the dynamic change trends of high-frequency and low-frequency signals and monitor the speed and amplitude of pressure fluctuations when the status recognition module detects that the pregnant woman is in a positional change state. The signal response regulation unit regulates high-frequency and low-frequency pressure fluctuations when the dynamic evaluation unit detects that the signal change exceeds a threshold. The feedback adjustment optimization unit collects the pressure value output by the stabilization regulation unit or the signal response regulation unit in real time, evaluates the difference between the current applied pressure and the ideal pressure level, and dynamically adjusts the pressure value using the following formula:

[0010]

[0011] In the formula, P adaptive (t+Δt) represents the pressure value output at time t+Δt, P fine (t) represents the pressure value output at time t, P desired Let θ be the target pressure value and θ be the adjustment factor. Optimization is performed using the gradient descent principle.

[0012] As a further aspect of the present invention, the step of optimizing the adjustment factor through gradient descent is as follows:

[0013] Step A1: Based on the current status of the pregnant woman output by the status recognition module, set the initial adjustment factor;

[0014] Step A2: The difference between the current applied pressure and the target pressure is collected in real time through the feedback adjustment and optimization unit.

[0015] Step A3: Using the feedback information, update the adjustment factor using the gradient descent update formula;

[0016] Step A4: Adjust the learning rate according to the pregnant woman's status. When the status recognition module detects that the pregnant woman is changing her position, it automatically increases the learning rate; when the pregnant woman is in a resting state, it automatically decreases the learning rate.

[0017] Step A5: Based on the updated adjustment factor, recalculate and adjust the pressure value.

[0018] As a further aspect of the present invention, in the stable control mode, when the low-frequency signal energy exceeds its preset range, the stable control unit gradually increases the applied pressure value until the pressure reaches the set range; when the high-frequency signal energy is lower than its preset range, it maintains the current pressure output.

[0019] As a further aspect of the present invention, the dynamic signal trend evaluation unit is used to analyze the changing trends of high-frequency and low-frequency signals over time. By calculating the difference between the current signal energy and the signal energy at the previous moment, the rate of change of pressure fluctuation is evaluated, and the result is transmitted to the signal response control unit, including the following steps:

[0020] Step B1: The dynamic signal trend evaluation unit receives real-time high-frequency and low-frequency signal data transmitted by the frequency domain signal analysis module and obtains the signal energy value at the current moment.

[0021] Step B2, calculate the rate of change of signal energy, the formula is:

[0022]

[0023] In the formula, ΔE(f) is the rate of change of the signal energy at frequency f, and E current (f) represents the signal energy at the current time t, E previous (f) represents the signal energy at the previous time t-Δt, where Δt is the time interval;

[0024] Step B3: When the rate of change of the high-frequency signal exceeds the set high-frequency signal threshold range, the signal is transmitted to the signal response control unit to perform the operation of reducing pressure; when the rate of change of the low-frequency signal falls within the set low-frequency signal threshold range, the signal is transmitted to the signal response control unit to perform the operation of increasing pressure.

[0025] As a further aspect of the present invention, the signal response control unit receives the judgment result from the dynamic evaluation unit and amplifies or suppresses the changes in high-frequency and low-frequency signals through a control formula. When the rate of change of the high-frequency signal exceeds a set threshold, the applied pressure is reduced; when the rate of change of the low-frequency signal falls within the set low-frequency signal threshold range, the pressure is increased. The control formula is as follows:

[0026]

[0027] In the formula, P fine (t) represents the actual pressure value output at time t, P initial Let ΔE(f) be the initial pressure value. high ) represents the change value of the high-frequency signal, ΔE(f) low ) represents the change value of the low-frequency signal, and γ and λ are the parameters for controlling the adjustment of the high-frequency and low-frequency signals, respectively.

[0028] As a further aspect of the present invention, the frequency domain signal analysis module is used to receive the time domain pressure signal generated by the pressure sensor, and preprocess the signal through a low-pass filter to remove noise and smooth it; the preprocessed time domain pressure signal is converted into a frequency domain signal through Fourier transform, and high-frequency components and low-frequency components are extracted to represent rapid pressure fluctuations and stable pressure fluctuations, respectively; after the Fourier transform generates the frequency domain signal, the energy value of a specific frequency band is calculated using the spectral energy calculation formula.

[0029] As a further aspect of the present invention, the state recognition module receives spectral energy data from the frequency domain signal analysis module, including the energy values ​​of high-frequency and low-frequency signals, and combines it with historical data to identify the current physiological state of the pregnant woman, including resting state and postural change state, through a support vector machine algorithm; the state recognition module transmits the classification results to the pressure regulation module to determine whether the system enters a stable regulation mode or a rapid response mode.

[0030] As a further aspect of the present invention, identifying the current physiological state of a pregnant woman using a support vector machine algorithm includes the following steps:

[0031] Step S1: The state recognition module receives real-time spectrum energy distribution data output from the frequency domain signal analysis module, including the energy values ​​of high-frequency signals and low-frequency signals.

[0032] Step S2: The state recognition module stores the current energy value and retrieves the high-frequency and low-frequency signal energy values ​​from the previous moment, combining them with historical data to form a complete time series.

[0033] Step S3: After data input, the received high-frequency and low-frequency signal energy is preprocessed;

[0034] Step S4: Based on the changing trends of high-frequency and low-frequency signal energy, construct a feature vector including the current high-frequency and low-frequency signal values ​​and historical high-frequency and low-frequency signal values;

[0035] Step S5: The state recognition module uses a pre-trained support vector machine classification model to classify the input feature vectors.

[0036] Step S6: Based on the output of the support vector machine classification algorithm, the state recognition module classifies the pregnant woman's current state as either a resting state or a postural change state, and outputs the classification result to the pressure control module.

[0037] Compared with the prior art, the beneficial effects of the pressure management system for postpartum abdominal incision healing in pregnant women of the present invention are as follows:

[0038] This invention combines a pressure sensor with a frequency domain signal analysis module to accurately capture pressure fluctuations and uses a state recognition module to determine whether the patient is at rest or in a different position. In existing technologies, pressurization devices typically provide a fixed pressure and cannot dynamically adjust according to individual conditions. However, the system of this invention has an adaptive control function, automatically optimizing pressure output when the patient is active or at rest, thereby reducing pressure fluctuations at the abdominal incision site.

[0039] This invention introduces a feedback adjustment and optimization unit, which can accurately assess the gap between the current applied pressure and the target pressure by collecting data in real time, and adjust the pressure based on the gradient descent principle. This function overcomes the problems of lag and insufficient precision in the response of traditional pressurization devices, realizing real-time dynamic control of pressure. By combining trend analysis of high-frequency and low-frequency signals, this invention can effectively avoid insufficient or excessive pressure, ensuring that the applied pressure is always within the most suitable range, thereby improving the stability and effectiveness of the pressurization device under various activity states. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of a pressure management system for postpartum abdominal incision healing in pregnant women, according to the present invention.

[0041] Figure 2 This is a flowchart of the status identification and pressure regulation of a pressure management system for postpartum abdominal incision healing in pregnant women, as described in this invention.

[0042] Figure 3 This is a schematic diagram of pressure monitoring and adjustment for a pressure management system for postpartum abdominal incision healing in pregnant women, according to the present invention.

[0043] In the diagram, (1) display; (2) external zero-point adjustment; (3) cable connector; (4) connector; (5) cable; (6) rubber bag; (7) rubber tube; (8) pressure sensor;

[0044] PEAK: Peak value; ON: Startup. Detailed Implementation

[0045] The technical solutions of this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0046] Example 1

[0047] A pressure management system for postpartum abdominal incision healing in pregnant women includes a pressure sensor, a rubber tube, a rubber bag, a frequency domain signal analysis module, a status recognition module, and a pressure control module.

[0048] In this embodiment of the invention, the pressure sensor is connected to the frequency domain signal analysis module, the frequency domain signal analysis module is connected to the state recognition module, the state recognition module is connected to the pressure control module, the pressure control module is connected to the pressure bag, and the rubber tube connects the pressure sensor and the pressure bag.

[0049] The frequency domain signal analysis module is used to receive the time domain pressure signal generated by the pressure sensor, convert it into a frequency domain signal through Fourier transform, and calculate the signal energy distribution in different frequency ranges.

[0050] The frequency domain signal analysis module in this embodiment of the invention receives the time domain pressure signal generated by the pressure sensor, preprocesses the signal using a low-pass filter to remove noise and smooth it for subsequent frequency domain analysis, and converts the preprocessed time domain pressure signal into a frequency domain signal using a Fourier transform to extract high-frequency and low-frequency components, representing rapid and stable pressure fluctuations, respectively. After generating the frequency domain signal using the Fourier transform, the energy value of a specific frequency band is calculated using the spectral energy calculation formula to further determine the intensity and frequency characteristics of the pressure fluctuation. Through the energy distribution, the system can further determine the intensity and frequency characteristics of the pressure fluctuation, as shown in the formula:

[0051]

[0052] In the formula, E(f) is the energy value within the frequency range f, f1 and f2 are the upper and lower frequency limits for energy calculation, and |F′(ω)| 2 Let F′(ω) be the square of the magnitude of the frequency domain signal, representing the signal strength within that frequency range;

[0053] This formula is used to calculate the energy in a specific frequency band, helping the system understand the amplitude and frequency of pressure fluctuations.

[0054] The formula for the Fourier transform in this embodiment of the invention is as follows:

[0055]

[0056] In the formula, F′(ω) is the frequency domain signal, P(t) is the pressure signal at time t, ω is the angular frequency, and e -iωt This represents a complex exponential function, used to convert time-domain signals into frequency-domain signals;

[0057] Through this transformation, the system can extract accurate frequency information, providing a foundation for subsequent state recognition and pressure regulation.

[0058] The state recognition module is used to identify the current physiological state of the pregnant woman based on the spectral information output by the frequency domain signal analysis module and in combination with a pre-trained classification model, including the resting state and the positional change state; when the recognition result is the resting state, it enters the stable control mode, and when the recognition result is the positional change state, it enters the rapid response mode.

[0059] In this embodiment of the invention, the state recognition module receives spectral energy data from the frequency domain signal analysis module, including the energy values ​​of high-frequency and low-frequency signals, and combines it with historical data to identify the current physiological state of the pregnant woman, including resting state and postural change state, through a support vector machine algorithm. The state recognition module transmits the classification results to the pressure regulation module to determine whether the system enters a stable regulation mode or a rapid response mode.

[0060] The method of identifying the current physiological state of a pregnant woman using the support vector machine algorithm in this embodiment of the invention includes the following steps:

[0061] Step S1: The state recognition module receives real-time spectrum energy distribution data output from the frequency domain signal analysis module, including the energy values ​​of high-frequency signals and low-frequency signals.

[0062] Step S2: The state recognition module stores the current energy value and calls up the high-frequency and low-frequency signal energy values ​​from the previous moment. It combines the energy values ​​with historical data to form a complete time series, providing additional reference information to enhance the accuracy of the current state recognition.

[0063] Step S3: After data input, the received high-frequency and low-frequency signal energy is preprocessed;

[0064] Step S4: Based on the changing trends of high-frequency and low-frequency signal energy, construct a feature vector including the current high-frequency and low-frequency signal values ​​and historical high-frequency and low-frequency signal values;

[0065] Step S5: The state recognition module uses a pre-trained support vector machine classification model to classify the input feature vectors.

[0066] Step S6: Based on the output of the support vector machine classification algorithm, the state recognition module classifies the pregnant woman's current state as either a resting state or a postural change state, and outputs the classification result to the pressure control module.

[0067] In this embodiment of the invention, the support vector machine algorithm maps the input multidimensional signal data to a high-dimensional space through a kernel function, and based on the training dataset, classifies the pregnant woman's current physiological state into one of two states: resting state or postural change state. The classification formula of the support vector machine algorithm is as follows:

[0068]

[0069] In the formula, C represents the classification result, and α i For the support vector coefficients, K(x) i (x, ) is the kernel function, x i Here, x represents historical data, and x represents the current signal feature vector.

[0070] The pressure control module is used to dynamically adjust the pressure applied to the abdominal incision of the pregnant woman based on the status information output by the status recognition module and the spectrum data provided by the frequency domain signal analysis module, and optimizes the control process in real time through a feedback mechanism. It includes a stable control unit, a dynamic signal trend evaluation unit, a signal response control unit, and a feedback adjustment optimization unit.

[0071] In this embodiment of the invention, the stabilization control unit is used to adjust the pressure value by executing the stabilization control mode when the state recognition module detects that the pregnant woman is in a resting state.

[0072] In the stable control mode of this invention, the stable control unit sets the pressure adjustment rate to no more than 10% per second of the current pressure value to ensure the stability and gentleness of the control. When the energy value of the low-frequency signal exceeds 10% of its average value, the system will gradually increase the applied pressure value, with the increase set at 5-10% per second of the initial pressure value, until the pressure reaches the ideal range to promote blood circulation and healing of the abdominal incision. When the energy value of the high-frequency signal is lower than 5% of its average value, the system does not need to respond quickly, but maintains the current pressure unchanged to avoid unnecessary pressure fluctuations and maintain the current stable pressure output.

[0073] In this embodiment of the invention, the dynamic signal trend evaluation unit is used to execute a fast response mode when the state recognition module detects that the pregnant woman is in a positional change state, analyze the dynamic change trend of high-frequency and low-frequency signals, and monitor the speed and amplitude of pressure fluctuations.

[0074] The dynamic signal trend evaluation unit in this embodiment of the invention is used to analyze the changing trends of high-frequency and low-frequency signals over time. By calculating the difference between the current signal energy and the signal energy at the previous moment, it evaluates the rate of change of pressure fluctuation and transmits the result to the signal response control unit, including the following steps:

[0075] Step B1: The dynamic signal trend evaluation unit receives real-time high-frequency and low-frequency signal data transmitted by the frequency domain signal analysis module and obtains the signal energy value at the current moment. Among them, the high-frequency signal represents the rapid fluctuation of the pregnant woman's abdominal pressure, and the low-frequency signal represents the relatively stable fluctuation of the pregnant woman's abdominal pressure.

[0076] Step B2: Calculate the rate of change of signal energy. The rate of change is analyzed by calculating the difference between the signal energy at the current moment and the signal energy at the previous moment. The formula is:

[0077]

[0078] In the formula, ΔE(f) is the rate of change of the signal energy at frequency f, and E current (f) represents the signal energy at the current time t, E previous (f) represents the signal energy at the previous time t-Δt, where Δt is the time interval;

[0079] In step B3, when the rate of change of the high-frequency signal exceeds the set high-frequency signal threshold range, the system determines that the pregnant woman's position has changed drastically. At this time, the signal is transmitted to the signal response control unit to perform the operation of reducing pressure in order to avoid excessive pressure on the abdominal incision. When the rate of change of the low-frequency signal falls within the set low-frequency signal threshold range, the signal is transmitted to the signal response control unit to perform the operation of increasing pressure in order to ensure that the pressure adjustment can promote the healing of the abdominal incision and blood circulation.

[0080] In this embodiment of the invention, after the pregnant woman changes position, some pressure has stabilized, but the system still needs to make appropriate adjustments to the remaining pressure. At this time, the system will gradually increase the pressure while the rate of change of the low-frequency signal falls within the threshold range, ensuring sufficient pressure to promote wound healing without excessive fluctuations, thus promoting healing and blood circulation.

[0081] In this embodiment of the invention, the signal response control unit is used to control high-frequency and low-frequency pressure fluctuations when the dynamic evaluation unit detects that the signal change exceeds a threshold.

[0082] In this embodiment of the invention, the signal response control unit receives the judgment result from the dynamic evaluation unit and amplifies or suppresses changes in high-frequency and low-frequency signals through a control formula to adapt to the changes in the pregnant woman's condition. When the rate of change of the high-frequency signal exceeds a set threshold, the applied pressure is reduced to prevent excessive pressure on the incision. When the rate of change of the low-frequency signal falls within the set low-frequency signal threshold range, the pressure is increased to promote blood circulation and help the incision heal. The control formula is as follows:

[0083]

[0084] In the formula, P fine (t) represents the actual pressure value output at time t, P initial Let ΔE(f) be the initial pressure value. high ) represents the change value of the high-frequency signal, ΔE(f) low ) represents the change value of the low-frequency signal, and γ and λ are the parameters for controlling the adjustment of the high-frequency and low-frequency signals, respectively.

[0085] In this embodiment of the invention, the feedback adjustment and optimization unit collects the pressure value output by the stabilization control unit or the signal response control unit in real time, evaluates the difference between the current applied pressure and the ideal pressure level, and dynamically adjusts the pressure value. The formula is as follows:

[0086]

[0087] In the formula, P adaptive (t+Δt) represents the pressure value output at time t+Δt, P fine (t) represents the pressure value output at time t, P desired Let θ be the target pressure value and θ be the adjustment factor. Optimization is performed using the gradient descent principle.

[0088] The feedback adjustment and optimization unit in this embodiment of the invention plays a crucial role in maintaining the accuracy of pressure applied to the abdominal region, enabling the system to dynamically respond to real-time feedback and maintain an appropriate and stable pressure level. The real-time feedback mechanism ensures that the system responds to even minor changes in pressure, thereby improving patient comfort, promoting optimal wound healing, and preventing discomfort or complications caused by excessive pressure.

[0089] The step of optimizing the adjustment factor by gradient descent in this embodiment of the invention is as follows:

[0090] Step A1: Based on the current status of the pregnant woman output by the status recognition module, set the initial adjustment factor;

[0091] Step A2: The difference between the current applied pressure and the target pressure is collected in real time through the feedback adjustment and optimization unit.

[0092] Step A3: Using feedback information, calculate the error between the current pressure and the target pressure. Based on the error function, update the adjustment factor using a gradient algorithm. The update formula is as follows:

[0093]

[0094] In the formula, θ(t+1) is the adjustment factor at time t+1, θ(t) is the adjustment factor at time t, and η is the learning rate, used to control the stride. P is the gradient of the error function with respect to the adjustment factor. desired P is the target pressure value. fine (t) represents the actual pressure value output at time t;

[0095] Step A4: Adjust the learning rate according to the pregnant woman's status. When the status recognition module detects a change in the pregnant woman's position, it automatically increases the learning rate to speed up the response of pressure adjustment; when the pregnant woman is in a resting state, it automatically decreases the learning rate to ensure more stable pressure adjustment.

[0096] Step A5: Based on the updated adjustment factor, recalculate and adjust the pressure value.

[0097] Example 2

[0098] like Figure 3 The diagram shown is a pressure monitoring and adjustment schematic of a pressure management system for postpartum abdominal incision healing in pregnant women according to the present invention.

[0099] In the diagram, (1) display; (2) external zero-point adjustment; (3) cable connector; (4) connector; (5) cable; (6) rubber bag; (7) rubber tube; (8) pressure sensor;

[0100] In this embodiment of the invention, the display is used to show the current pressure level; the external zero-point adjustment is used to adjust the zero point of the device or for calibration to ensure measurement accuracy; the cable connector represents the fixed joint portion between the cable and the device, used to maintain the strength and sealing of the connection; the connector is used for cable connection in the instrument; the cable is used to transmit signals or power; the rubber bag is used to apply the actual pressure to the pregnant woman's abdomen during pressure regulation; and the rubber tube is used to connect the sensor and the rubber band.

[0101] In this embodiment of the invention, a pressure sensor collects real-time pressure signals from the pregnant woman's abdomen via a rubber tube and a rubber bag, and transmits this time-domain signal to a frequency-domain signal analysis module. This module preprocesses the signal using a low-pass filter and converts it to a frequency-domain signal using Fourier transform, analyzing the high-frequency and low-frequency components. Subsequently, as... Figure 2 The diagram shows a flow chart of the state recognition and pressure regulation of a pressure management system for postpartum abdominal incision healing in pregnant women according to the present invention. The state recognition module determines whether the mother is in a resting state or a positional change state based on the spectral information and a pre-trained classification model, and selects to enter either a stable regulation mode or a rapid response mode. In the stable regulation mode, the signal value analysis unit adjusts the pressure according to the changes in low-frequency signal energy, gradually increasing or maintaining the pressure to promote incision healing. In the rapid response mode, the dynamic signal trend evaluation unit analyzes the rate of change of high-frequency signals and transmits the analysis results to the signal response regulation unit to reduce the applied pressure in a timely manner and prevent excessive pressure from compressing the incision.

[0102] In this embodiment of the invention, the feedback adjustment and optimization unit collects the pressure value output by the stability control unit or the signal response control unit in real time, uses the adjustment factor to optimize the pressure value, ensures that the ideal pressure level is reached, and finally completes the entire pressurization management control process.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A pressure management system for postpartum abdominal incision healing in pregnant women, comprising a pressure sensor, characterized in that, It also includes a frequency domain signal analysis module, a state recognition module, and a pressure control module. The pressure sensor is connected to the frequency domain signal analysis module, which is connected to the state recognition module, and the state recognition module is connected to the pressure control module. The state recognition module is used to identify the pregnant woman's current physiological state, including resting state and postural change state, based on the spectral information output by the frequency domain signal analysis module and a pre-trained classification model. When the identification result is a resting state, it enters a stable control mode; when the identification result is a postural change state, it enters a rapid response mode. The pressure regulation module dynamically adjusts the pressure applied to the abdominal incision of the pregnant woman based on the status information output by the status recognition module and the spectrum data provided by the frequency domain signal analysis module, and optimizes the regulation process through a real-time feedback mechanism. It includes a stabilization regulation unit, a dynamic signal trend evaluation unit, a signal response regulation unit, and a feedback adjustment optimization unit. The stabilization regulation unit adjusts the pressure value in a stable regulation mode when the status recognition module detects that the pregnant woman is in a resting state. The dynamic signal trend evaluation unit executes a rapid response mode to analyze the dynamic change trends of high-frequency and low-frequency signals and monitor the speed and amplitude of pressure fluctuations when the status recognition module detects that the pregnant woman is in a positional change state. The signal response regulation unit regulates high-frequency and low-frequency pressure fluctuations when the dynamic evaluation unit detects that the signal change exceeds a threshold. The feedback adjustment optimization unit collects the pressure value output by the stabilization regulation unit or the signal response regulation unit in real time, evaluates the difference between the current applied pressure and the ideal pressure level, and dynamically adjusts the pressure value using the following formula: In the formula, P adaptive (t+Δt) represents the pressure value output at time t+Δt, P fine (t) represents the pressure value output at time t, P desired Let θ be the target pressure value and θ be the adjustment factor. Optimization is performed using the gradient descent principle.

2. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1, characterized in that, The steps for optimizing the adjustment factor using gradient descent are as follows: Step A1: Based on the current status of the pregnant woman output by the status recognition module, set the initial adjustment factor; Step A2: The difference between the current applied pressure and the target pressure is collected in real time through the feedback adjustment and optimization unit. Step A3: Using the feedback information, update the adjustment factor using the gradient descent update formula; Step A4: Adjust the learning rate according to the pregnant woman's status. When the status recognition module detects that the pregnant woman is in a positional change, it automatically increases the learning rate; when the pregnant woman is in a resting state, it automatically decreases the learning rate. Step A5: Based on the updated adjustment factor, recalculate and adjust the pressure value.

3. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1, characterized in that, In the stable control mode, when the low-frequency signal energy exceeds its preset range, the stable control unit gradually increases the applied pressure value until the pressure reaches the set range; when the high-frequency signal energy is below its preset range, it maintains the current pressure output.

4. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1, characterized in that, The dynamic signal trend assessment unit is used to analyze the changing trends of high-frequency and low-frequency signals over time. By calculating the difference between the current signal energy and the signal energy at the previous moment, it assesses the rate of change of pressure fluctuation and transmits the result to the signal response control unit, including the following steps: Step B1: The dynamic signal trend evaluation unit receives real-time high-frequency and low-frequency signal data transmitted by the frequency domain signal analysis module and obtains the signal energy value at the current moment. Step B2, calculate the rate of change of signal energy, the formula is: In the formula, ΔE(f) is the rate of change of the signal energy at frequency f, and E current (f) represents the signal energy at the current time t, E previous (f) represents the signal energy at the previous time t-Δt, where Δt is the time interval; Step B3: When the rate of change of the high-frequency signal exceeds the set high-frequency signal threshold range, the signal is transmitted to the signal response control unit to perform the operation of reducing pressure; when the rate of change of the low-frequency signal falls within the set low-frequency signal threshold range, the signal is transmitted to the signal response control unit to perform the operation of increasing pressure.

5. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1, characterized in that, The signal response control unit receives the judgment result from the dynamic evaluation unit and amplifies or suppresses changes in high-frequency and low-frequency signals using a control formula. When the rate of change of the high-frequency signal exceeds a set threshold, the applied pressure is reduced; when the rate of change of the low-frequency signal falls within a set low-frequency signal threshold range, the pressure is increased. The control formula is as follows: In the formula, P fine (t) represents the actual pressure value output at time t, P initial Let ΔE(f) be the initial pressure value. high ) represents the change value of the high-frequency signal, ΔE(f) low ) represents the change value of the low-frequency signal, and γ and λ are the parameters for controlling the adjustment of the high-frequency and low-frequency signals, respectively.

6. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1, characterized in that, The frequency domain signal analysis module is used to receive the time domain pressure signal generated by the pressure sensor, and preprocess the signal through a low-pass filter to remove noise and smooth it; the preprocessed time domain pressure signal is converted into a frequency domain signal through Fourier transform, and high-frequency and low-frequency components are extracted to represent rapid pressure fluctuations and stable pressure fluctuations, respectively; after the Fourier transform generates the frequency domain signal, the energy value of a specific frequency band is calculated using the spectral energy calculation formula.

7. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1, characterized in that, The state recognition module receives spectral energy data from the frequency domain signal analysis module, including the energy values ​​of high-frequency and low-frequency signals, and combines it with historical data to identify the pregnant woman's current physiological state, including resting state and postural changes, through a support vector machine algorithm. The status recognition module transmits the classification results to the pressure control module to determine whether the system enters a stable control mode or a rapid response mode.

8. A pressure management system for postpartum abdominal incision healing in pregnant women according to claim 1 or 7, characterized in that, Identifying a pregnant woman's current physiological state using the support vector machine algorithm includes the following steps: Step S1: The state recognition module receives real-time spectrum energy distribution data output from the frequency domain signal analysis module, including the energy values ​​of high-frequency signals and low-frequency signals. Step S2: The state recognition module stores the current energy value and retrieves the high-frequency and low-frequency signal energy values ​​from the previous moment, combining them with historical data to form a complete time series. Step S3: After data input, the received high-frequency and low-frequency signal energy is preprocessed; Step S4: Based on the changing trends of high-frequency and low-frequency signal energy, construct a feature vector including the current high-frequency and low-frequency signal values ​​and historical high-frequency and low-frequency signal values; Step S5: The state recognition module uses a pre-trained support vector machine classification model to classify the input feature vectors. Step S6: Based on the output of the support vector machine classification algorithm, the state recognition module classifies the pregnant woman's current state as either a resting state or a postural change state, and outputs the classification result to the pressure control module.

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