A system for integrated perfusion and negative pressure aspiration of a renal pelvis pressure

Abstract

The present application relates to the technical field of liquid flow control, and particularly relates to a kind of integrated perfusion and negative pressure suction pyelostasis control method, comprising: in the embodiment of the present application, according to the size of renal pelvis pressure and the difference of renal pelvis pressure at adjacent time, determine the severity of each time suspected problem, according to the difference of renal pelvis pressure in different period, and the change related situation of each dimension data in the same period, determine the corresponding pressure effective dangerous fluctuation factor of each time, to determine the control weight of each time renal pelvis pressure, to constitute time weight time sequence, and then output perfusion pump flow adjustment instruction and negative pressure suction pump flow adjustment instruction through fuzzy controller, realize pyelostasis control.The present application enhances the identification ability of the change trend of renal pelvis pressure data in key period by introducing time weight, so as to more accurately and more stably maintain the constant of renal pelvis pressure.

Description

Technical Field

[0001] This invention relates to the field of liquid flow control technology, specifically to a renal pelvis pressure stabilization control system that integrates perfusion and negative pressure suction. Background Technology

[0002] The integrated perfusion and negative pressure suction method for stabilizing renal pelvis pressure is a real-time closed-loop pressure control technology applied in minimally invasive urological surgeries (such as ureteroscopic lithotripsy). Its core principle is to precisely stabilize the intrarenal pelvis pressure within a safe physiological range by synchronously monitoring and dynamically adjusting the perfusion and negative pressure suction flow rates, thus preventing fluid reflux, infection, and kidney damage caused by high pressure. It deeply integrates fluid dynamics, sensor technology, and intelligent control algorithms, solving the problem of uncontrolled renal pelvis pressure in traditional surgeries through a three-in-one closed-loop system of perfusion-suction-pressure measurement. This significantly improves lithotripsy efficiency while ensuring surgical safety.

[0003] Existing problems: Renal pelvic pressure is subject to real-time disturbances from various factors such as perfusion flow, suction flow, stone blockage, instrument movement, and respiratory motion, resulting in non-stationary, non-linear, and highly disturbed pressure changes. Current control methods rely solely on the current pressure deviation and its rate of change, which can lead to excessive output adjustments during pressure increases and insufficient compensation during pressure decreases, resulting in renal pelvic pressure oscillations and repeated overshoot. Furthermore, the fluid loop from perfusion fluid entering the renal pelvis through tubing to its discharge under negative pressure exhibits significant transmission lag, and a delay exists between pump speed adjustment commands and renal pelvic pressure responses. This makes conventional control prone to lag, under-adjustment, or over-adjustment, hindering precise and stable control of renal pelvic pressure. Summary of the Invention

[0004] This invention provides a method for stabilizing and controlling renal pelvis pressure by integrating perfusion and negative pressure suction, in order to solve existing problems.

[0005] The renal pelvis pressure stabilization control method integrating perfusion and negative pressure suction of the present invention adopts the following technical solution: One embodiment of the present invention provides a method for stabilizing and controlling renal pelvis pressure by integrating perfusion and negative pressure suction, the method comprising the following steps: Obtain time-series sequences of renal pelvis pressure, infusion pump flow rate, and negative pressure suction pump flow rate; The severity of the suspected problem at each time point is determined based on the magnitude of renal pelvis pressure in the renal pelvis pressure time series and the difference in renal pelvis pressure between adjacent time points; Based on the differences in renal pelvis pressure at different time points in the renal pelvis pressure time series, the degree of local periodic regularity disorder corresponding to each time point is determined; based on the correlation of data changes in the renal pelvis pressure time series, infusion pump flow time series, and negative pressure suction pump flow time series within the same time period, the degree of effective inducing pressure fluctuation corresponding to each time point is determined; based on the magnitude of the degree of local periodic regularity disorder and the degree of effective inducing pressure fluctuation, the effective risk fluctuation factor of pressure corresponding to each time point is determined. The severity of the suspected problem is adjusted based on the pressure effective hazard fluctuation factor to determine the renal pelvis pressure control weight at each moment; the renal pelvis pressure control weights at all moments are arranged in chronological order to form a time-weighted time sequence; the renal pelvis pressure time sequence and the time-weighted time sequence are input into the fuzzy controller to output the perfusion pump flow rate adjustment command and the negative pressure suction pump flow rate adjustment command to achieve stable control of renal pelvis pressure.

[0006] Furthermore, the specific steps for determining the severity of the suspected problem at each moment are as follows: In the renal pelvis pressure time series, the absolute value of the difference between the renal pelvis pressure and the preset target renal pelvis pressure at each moment is calculated, and the ratio of the absolute value of the difference to the preset target renal pelvis pressure is recorded as the pressure deviation at each moment. The moment when the pressure deviation exceeds the preset deviation threshold is defined as the pressure deviation moment, and the pressure deviation period is composed of adjacent pressure deviation moments. If each moment is not a pressure deviation moment, then the pressure deviation degree at each moment is recorded as the pressure deviation performance at each moment. If each moment is a pressure deviation moment, calculate the sum of the normalized value of the length of the pressure deviation period to which each moment belongs and a preset constant, and multiply the sum by the pressure deviation degree at each moment to record the pressure deviation performance at each moment. The degree of abrupt change in renal pelvis pressure at each moment is determined based on the difference in renal pelvis pressure between adjacent moments in the renal pelvis pressure time series. The severity of the suspected problem at each moment is determined based on the observed pressure deviation and the magnitude of the sudden change in renal pelvis pressure.

[0007] Furthermore, the specific steps for determining the degree of sudden change in renal pelvis pressure at each moment are as follows: Select the first few preset time range thresholds that are closest to each time interval, and combine them with each time interval to form the target time corresponding to each time interval. The mean of the absolute values ​​of the differences in renal pelvis pressure between all adjacent target times is recorded as the degree of sudden change in renal pelvis pressure corresponding to each time interval.

[0008] Furthermore, the specific steps for determining the severity of the suspected problem at each moment based on the pressure deviation and the magnitude of the sudden change in renal pelvis pressure include the following: The mean of the normalized values ​​of the pressure deviation from the performance at each moment and the normalized values ​​of the sudden change in renal pelvis pressure is recorded as the suspected problem severity at each moment.

[0009] Furthermore, the specific steps for determining the local periodicity disorder at each moment are as follows: Select the first few preset time range thresholds that are closest to the time interval of each time moment, and combine them with each time moment to refer to the target time moment corresponding to each time moment; In the renal pelvis pressure time series, the renal pelvis pressure at all target times is arranged in chronological order to form the local renal pelvis pressure sequence corresponding to each time point; Calculate the Pearson correlation coefficient between the local renal pelvis pressure sequence at each time point and the local renal pelvis pressure sequences at other time points. Select the maximum value among all Pearson correlation coefficient values ​​and define the normalized value of the maximum value as the repeatability of the local renal pelvis pressure change trend at each time point. Calculate the Euclidean distance between the local renal pelvis pressure sequence at each time step and the local renal pelvis pressure sequences at other time steps. Select the minimum value among all Euclidean distances and define the complement normalized value of the minimum value as the local renal pelvis pressure consistency at each time step. The degree of disorder of the local periodic regularity at each moment is determined based on the repeatability of the local renal pelvis pressure change trend and the consistency of the local renal pelvis pressure.

[0010] Furthermore, the specific steps for determining the degree of local periodic regularity disorder at each moment based on the repeatability of the local renal pelvis pressure change trend and the consistency of the local renal pelvis pressure are as follows: The complement of the mean of the repeatability of the local renal pelvis pressure change trend and the consistency of the local renal pelvis pressure at each time moment is denoted as the local periodic regularity disorder at each time moment.

[0011] Furthermore, the specific steps for determining the effective causative factor degree of pressure fluctuation at each moment are as follows: In the injection pump flow time series, the injection pump flow rates at all target times are arranged in chronological order to form a local sequence of injection pump flow rates at each time point. In the time series of negative pressure suction pump flow rate, the negative pressure suction pump flow rate at all target times is arranged in chronological order to form a local sequence of negative pressure suction pump flow rate at each time. Calculate the Pearson correlation coefficient between the local sequence of the injection pump flow rate and the local sequence of the negative pressure suction pump flow rate at each time point, and denote it as the positive synergy of the flow inflow and outflow at each time point; In the local sequence of the injection pump flow rate and the local sequence of the negative pressure suction pump flow rate at each moment, the difference between the injection pump flow rate and the negative pressure suction pump flow rate is calculated one by one according to the same ordinal position, and the flow difference sequence is constructed by combining them in sequence. The Pearson correlation coefficient between the flow difference sequence and the local renal pelvis pressure sequence at each time step is obtained and denoted as the flow difference pressure coupling correlation coefficient at each time step. Based on the magnitude of the correlation coefficient between the positive synergy of flow inflow and outflow and the pressure coupling of flow difference, the effective inducing factor of pressure fluctuation at each moment is determined.

[0012] Furthermore, the specific steps for determining the effective inducing factor of pressure fluctuation at each moment based on the magnitude of the correlation coefficient between the positive synergy of flow inflow and outflow and the pressure coupling of flow difference are as follows: The normalized value of the mean of the negative number of the positive synergy between flow inflow and outflow at each time point and the correlation coefficient between flow difference and pressure is denoted as the effective inducing factor degree of pressure fluctuation at each time point.

[0013] Furthermore, the specific steps for determining the effective pressure hazard fluctuation factor at each moment are as follows: The average of the local periodic regularity disorder and the effective inducing factor of pressure fluctuation at each moment is denoted as the effective risk fluctuation factor of pressure at each moment.

[0014] Furthermore, the specific steps for determining the renal pelvis pressure control weight at each moment are as follows: Calculate the sum of the effective risk fluctuation factor and the preset constant corresponding to each moment, and multiply the sum by the severity of the suspected problem at each moment, and record it as the renal pelvis pressure control weight at each moment.

[0015] The beneficial effects of the technical solution of the present invention are: In this embodiment of the invention, the severity of the suspected problem at each moment is determined based on the renal pelvis pressure magnitude in the renal pelvis pressure time series and the renal pelvis pressure difference between adjacent moments. Based on the renal pelvis pressure differences in different time periods in the renal pelvis pressure time series, as well as the correlation of data changes in the renal pelvis pressure time series, the perfusion pump flow time series, and the negative pressure suction pump flow time series within the same time period, the effective pressure risk fluctuation factor corresponding to each moment is determined, thereby determining the renal pelvis pressure control weight at each moment. This distinguishes between important fluctuations reflecting patient physiological disturbances, surgical instrument operation, or abnormal perfusion fluid properties, and unimportant weak interference fluctuations caused by sensor noise, microbubbles, etc. Only large, sudden, trending, or coupled fluctuations caused by respiration, coughing, instrument movement, lithotripsy energy release, non-Newtonian fluids, and tubing blockage are judged as important data, ensuring the accuracy of time weighting. The renal pelvic pressure control weights at all times are arranged chronologically to form a time-weighted time sequence. This time sequence, along with the time-weighted time sequence, is input into a fuzzy controller, which outputs perfusion pump flow rate adjustment commands and negative pressure suction pump flow rate adjustment commands to achieve stable renal pelvic pressure control. This invention introduces time weights into the integrated perfusion and negative pressure suction fuzzy control of renal pelvic pressure to highlight the dominant role of renal pelvic pressure data changes at critical moments, weaken the influence of data at non-critical moments, enhance the ability to identify renal pelvic pressure data change trends during critical periods, and improve system response speed, dynamic stability, and safety protection performance, thereby maintaining constant renal pelvic pressure more accurately and robustly. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the steps of a renal pelvis pressure stabilization control method integrating perfusion and negative pressure suction according to the present invention; Figure 2 A hierarchical closed-loop coupling control system for renal pelvis pressure that integrates perfusion and negative pressure suction. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a renal pelvis pressure stabilization control method integrating perfusion and negative pressure suction proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a renal pelvis pressure stabilization control method integrating perfusion and negative pressure suction provided by the present invention.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a method for stabilizing renal pelvis pressure by integrating perfusion and negative pressure suction, according to an embodiment of the present invention. The method includes the following steps: Step S001: Obtain the renal pelvis pressure time series, the infusion pump flow time series, and the negative pressure suction pump flow time series.

[0022] In this embodiment, the real-time renal pelvis pressure signal is directly acquired by a miniature pressure sensor implanted in the renal pelvis; the real-time flow rate of the infusion pump is obtained by detection by a flow sensor or indirectly calculated from the drive motor speed; the real-time flow rate of the negative pressure suction pump is acquired by a flow sensor in the suction tubing or estimated in real time by combining the pump speed and the negative pressure value. The sampling frequency of all data dimensions is 10Hz (Hertz). Based on the renal pelvis pressure sampling time, the infusion pump flow rate and the negative pressure suction pump flow rate are timestamped to ensure the consistency of the time sequence of multi-dimensional data.

[0023] Thus, the time series sequences of renal pelvis pressure, infusion pump flow rate, and negative pressure suction pump flow rate were obtained, and each time series was subjected to noise reduction processing by moving average filtering.

[0024] It should be noted that in this embodiment, a time-weighted fuzzy control algorithm is used to achieve real-time stable control of renal pelvis pressure. The real-time renal pelvis pressure signal is the core controlled variable and serves as the direct basis for fuzzy control and time weight allocation. The real-time flow rate of the perfusion pump is used for flow balance judgment and feedforward adjustment. The real-time flow rate of the negative pressure suction pump is linked to the perfusion flow rate to achieve a balance between fluid inflow and outflow within the renal pelvis. Both the moving average filtering and the time-weighted fuzzy control algorithm are well-known techniques, and their specific methods will not be described here.

[0025] It should be further explained that in this embodiment, real-time time series data for the first 1 to 5 seconds is collected as the basis for data analysis and baseline establishment. Subsequently, data for subsequent time periods is continuously collected, and a time-weighted fuzzy control algorithm is used to achieve real-time stable control of renal pelvis pressure.

[0026] Step S002: Determine the severity of the suspected problem at each time step based on the renal pelvis pressure magnitude in the renal pelvis pressure time sequence and the renal pelvis pressure difference between adjacent time steps.

[0027] It should be noted that renal pelvis pressure reflects the fluid filling status within the renal pelvis in real time and is a direct basis for judging high-pressure injury, fluid accumulation, tubing blockage, and poor drainage. The infusion pump flow rate is used to continuously infuse the renal pelvis with irrigation fluid, maintaining a clear surgical field during surgery, cooling surgical instruments, diluting pus and bleeding, and determining the inflow rate of renal pelvis fluid; it is a positive factor influencing renal pelvis pressure elevation. The negative pressure suction pump flow rate is used to actively aspirate irrigation fluid, lithotripsy, and bloody secretions from the renal pelvis, determining the outflow rate of renal pelvis fluid; it is a negative regulatory factor inhibiting pressure elevation, rapidly releasing pressure, and preventing fluid retention. Among these, renal pelvis pressure directly reflects the patient's renal pelvis safety status; the greater the deviation from the target value and the more severe the fluctuation, the higher the corresponding regulatory priority. The infusion pump flow rate directly affects the amount of fluid entering the renal pelvis, and the negative pressure suction pump flow rate directly affects the amount of fluid exiting; abnormal fluctuations in either will directly cause dynamic changes in renal pelvis pressure. Therefore, in this embodiment, the severity of suspected problems in the renal pelvis pressure time-series data is first quantitatively analyzed based on the fluctuation characteristics and deviation from the target value.

[0028] Therefore, the severity of the suspected problem at each time point is determined based on the magnitude of the renal pelvis pressure in the renal pelvis pressure time series and the difference in renal pelvis pressure between adjacent time points.

[0029] Step S003: Based on the differences in renal pelvis pressure at different time periods in the renal pelvis pressure time series, determine the degree of local periodic regularity disorder at each time point; based on the correlation of data changes in the renal pelvis pressure time series, the infusion pump flow time series, and the negative pressure suction pump flow time series within the same time period, determine the degree of effective inducing pressure fluctuation at each time point; based on the magnitude of the degree of local periodic regularity disorder and the degree of effective inducing pressure fluctuation, determine the effective risk fluctuation factor of pressure at each time point.

[0030] It should be noted that when fluctuations occur in the collected renal pelvis pressure, these fluctuations could be significant fluctuations reflecting patient physiological disturbances, surgical instrument manipulation, or abnormal perfusion fluid properties, or they could be minor, weak interference fluctuations caused by sensor noise, microbubbles, etc. Only large, sudden, trend-like, or coupled fluctuations caused by respiration, coughing, instrument movement, lithotripsy energy release, non-Newtonian fluids, or tubing blockage are considered important data. Therefore, further analysis is needed to determine whether the renal pelvis pressure fluctuations represent valid, dangerous fluctuations.

[0031] It should be further explained that in the renal pelvis pressure stabilization control system that integrates perfusion and negative pressure suction, the renal pelvis pressure changes periodically due to the periodic fluid delivery and suction effects of the perfusion pump and the negative pressure suction pump, combined with the elastic constraint characteristics of the renal pelvis cavity and the dynamic balance mechanism of the fluid inside the cavity. This results in a regular fluctuation in renal pelvis pressure during the continuous monitoring period.

[0032] First, based on the differences in renal pelvis pressure at different time points in the renal pelvis pressure time sequence, the degree of local periodic regularity disorder corresponding to each moment is determined.

[0033] It should be noted that the degree of local periodic regularity disorder is initially identified and determined as a dangerous fluctuation by comparing the local sequence of renal pelvic pressure time-series data. Further verification and comprehensive judgment are then performed by combining the coupling relationship between the infusion pump flow rate and the negative pressure suction pump flow rate on the intracavitary pressure, ultimately determining whether the pressure fluctuation is a valid dangerous fluctuation. The infusion pump flow rate and negative pressure suction pump flow rate are controllable output signals of the equipment, characterized by high operational stability and minimal environmental interference. They only reflect the transport and drainage conditions of fluids inside and outside the renal pelvis and cannot directly replace renal pelvic pressure as the core feedback quantity for closed-loop control. Renal pelvic pressure is a weak physiological pressure signal within the cavity, easily affected by various factors such as instrument operation disturbances, fluid dynamic impacts, and micro-movements of body tissues, resulting in significant signal fluctuations. Therefore, the temporal changes in the infusion pump flow rate and negative pressure suction pump flow rate can serve as effective reference data for identifying the causes of renal pelvic pressure fluctuations.

[0034] Then, based on the correlation of data changes in the renal pelvis pressure time series, the infusion pump flow time series, and the negative pressure suction pump flow time series within the same time period, the effective inducing factor degree of pressure fluctuation at each moment is determined.

[0035] Finally, based on the degree of disorder of the local periodic pattern and the degree of effective inducing pressure fluctuation at each moment, the effective risk fluctuation factor of pressure at each moment is determined.

[0036] Step S004: Adjust the severity of the suspected problem based on the pressure effective danger fluctuation factor to determine the renal pelvis pressure control weight at each moment; arrange the renal pelvis pressure control weights at all moments in chronological order to form a time-weighted time sequence; input the renal pelvis pressure time sequence and the time-weighted time sequence into the fuzzy controller, and output the perfusion pump flow rate adjustment command and the negative pressure suction pump flow rate adjustment command to achieve stable control of renal pelvis pressure.

[0037] First, the severity of suspected problems at each moment is adjusted based on the effective risk fluctuation factor of pressure at each moment, and the control weight of renal pelvis pressure at each moment is determined.

[0038] Then, the renal pelvis pressure control weights at all times are arranged in chronological order to form a time-weighted time series.

[0039] Finally, the fuzzy controller is input to the renal pelvis pressure time series and its corresponding time-weighted time series. By assigning differentiated weights to the pressure data at different times, the pressure change characteristics at key moments are highlighted. After fuzzy inference, the controller outputs perfusion pump flow rate adjustment commands and negative pressure suction pump flow rate adjustment commands, achieving precise and stable control of the renal pelvis pressure. The time-weighted fuzzy control algorithm is a well-known technique, and its specific method will not be described here. Specifically: The product of the renal pelvic pressure at each moment and the renal pelvic pressure control weight is recorded as the time-weighted adjusted renal pelvic pressure at that moment. The difference between the time-weighted adjusted renal pelvic pressure at each moment and the set target value is recorded as the time-weighted adjusted renal pelvic pressure deviation value at that moment. The difference between the time-weighted adjusted renal pelvic pressure deviation value at each moment and the time-weighted adjusted renal pelvic pressure deviation value at the previous moment is recorded as the time-weighted adjusted renal pelvic pressure deviation change rate at that moment. Therefore, based on the renal pelvic pressure time series sequence and its corresponding time-weighted time series sequence, the real-time time-weighted adjusted renal pelvic pressure deviation value and the renal pelvic pressure deviation change rate are obtained, input into the fuzzy controller, and output perfusion pump flow rate adjustment commands and negative pressure suction pump flow rate adjustment commands.

[0040] It should be noted that in this embodiment, the renal pelvis pressure hierarchical closed-loop coupling control system integrating perfusion and negative pressure suction, such as... Figure 2 As shown. Figure 2The system includes: (1) Sensing and acquisition layer: with pressure and flow signal acquisition unit as the core, it completes the synchronous acquisition of multi-source time-series signals of renal pelvis pressure, perfusion flow, and negative pressure suction flow, providing raw sensing data for full-domain control. (2) Data processing layer: composed of data preprocessing and time weight analysis unit, it undertakes signal noise reduction, time weight quantization assignment, and realizes raw data standardization and feature optimization. (3) Intelligent decision layer: with time weighted fuzzy inference control unit as the core, it receives weighted time-series data, completes fuzzy calculation, coupled logic judgment, and outputs precise control instructions through dual channels, which is the core decision center of the system. (4) Execution drive layer: the pump group drive execution unit is the terminal execution module, which receives control instructions and dynamically adjusts the working parameters of perfusion pump and negative pressure suction pump to form physical end control output.

[0041] Preferably, in some possible implementations of the embodiments of the present invention, in the renal pelvis pressure time sequence, the first... Constant renal pelvis pressure With preset target renal pelvis pressure The ratio of the absolute value of the difference to the preset target renal pelvis pressure , denoted as the Pressure deviation at any time .in, It is an absolute value function.

[0042] It should be noted that the clinical setting for the target renal pelvis pressure is 15 to 20 mmHg. In this embodiment, the preset target renal pelvis pressure is 18 mmHg, and this will be used as an example for description.

[0043] It should be further noted that in this embodiment, the first... The current moment is the last moment in the renal pelvis pressure time series. Subsequently, by analyzing the renal pelvis pressure time series, the control weight of the renal pelvis pressure at the current moment is determined, thereby completing real-time control.

[0044] The preset deviation threshold is 0.3, and this will be used as an example for explanation.

[0045] In the renal pelvis pressure time series, the moment when the pressure deviation exceeds a preset deviation threshold is defined as a pressure deviation moment, and adjacent pressure deviation moments constitute a pressure deviation period. A single, independently existing pressure deviation moment constitutes a separate pressure deviation period.

[0046] For the time sequence of renal pelvis pressure, the first At that moment, if the first If the time is not deviated from the pressure time, then the first time will be... Pressure deviation at any time , denoted as the Constant pressure deviation performance If the first If time is the time of pressure deviation, then the first... Constant pressure deviation performance The calculation formula is: ; In the formula, The first time series of renal pelvis pressure The length of the time period to which the pressure belongs. This represents the maximum value among all pressure deviation time intervals in the renal pelvis pressure time series. for The normalized value. In this embodiment, the preset constant is 1, and this will be used as an example for description.

[0047] It should be noted that the longer the period of pressure deviation, the more persistently the abnormally high or low pressure. Persistently high pressure indicates poor drainage and fluid accumulation, a state of chronic hypertension risk; persistently low pressure indicates excessive drainage, which can easily lead to renal pelvis collapse. Furthermore, the larger the pressure deviation value, the more significant the abnormal pressure deviation, and this should be given priority during the process of stabilizing renal pelvis pressure.

[0048] Preferably, in some possible implementations of the embodiments of the present invention, in the renal pelvis pressure time sequence, the sequence is selected that corresponds to the first... The 10 most recent time intervals, combined with the first... The time, collectively referred to as the first The target time corresponding to time t is the average of the absolute values ​​of the differences in renal pelvis pressure between all adjacent target times, denoted as the i-th time. The degree of sudden change in renal pelvis pressure at any given moment .

[0049] In this embodiment, the preset time range threshold is 10, and this will be used as an example for description.

[0050] It should be noted that a rapid and sudden increase or decrease in renal pelvis pressure within a short period of time, with an extremely high rate of pressure change, is often caused by conditions such as sheath blockage, lithotripsy, instantaneous impact of perfusion fluid, or poor negative pressure suction drainage. These conditions require close monitoring and intervention during the process of stabilizing and regulating renal pelvis pressure.

[0051] Preferably, in some possible implementations of the embodiments of the present invention, the first... Constant pressure deviation performance normalized value With the The degree of sudden change in renal pelvis pressure at any given moment normalized value The mean of is denoted as the th. The severity of the suspected problem at any given moment.

[0052] in, This represents the maximum value among all pressure deviations in the renal pelvis pressure time-series sequence. This represents the maximum value among all time points in the renal pelvis pressure time series, indicating the degree of abrupt change in renal pelvis pressure. It is a very small constant. This is used to prevent the denominator from being 0.

[0053] It should be noted that: the pressure deviation reflects the degree of danger of the pressure deviating from the safety target, and the degree of sudden change in renal pelvis pressure reflects the stability of pressure fluctuation. Both together determine the severity of the suspected problem at that moment. The larger the value, the more dangerous the pressure state at that moment, and the more priority control is required.

[0054] Preferably, in some possible implementations of the embodiments of the present invention, in the renal pelvis pressure time sequence, the first... The renal pelvis pressures at all target times corresponding to time point are arranged in chronological order to form the first... Local sequence of renal pelvis pressure at each time point.

[0055] Calculate the first one respectively The Pearson correlation coefficient between the local renal pelvic pressure sequence at time 1 and the local renal pelvic pressure sequences at all other time points was selected, and the maximum value among all Pearson correlation coefficients was used. normalized value Defined as the first Repeatability of local renal pelvis pressure change trends at different times.

[0056] It should be noted that in this embodiment, the preset avoidance step size is 10. When calculating the... When considering the Pearson correlation coefficient between the local renal pelvic pressure sequence at time 1 and the local renal pelvic pressure sequences at all other time points, the correlation coefficient between the other time points and the first time point is limited. The number of time intervals between moments should be greater than 10 to prevent the first... There is a spurious correlation between the local renal pelvic pressure sequence at time 1 and the local renal pelvic pressure sequences at other time 2 due to excessive data overlap.

[0057] It should be further noted that the Pearson correlation coefficient ranges from -1 to 1, therefore... As The normalized value. In this embodiment, when calculating the Pearson correlation coefficient between two sequences, if the variance of either sequence is 0, it is determined that the sequence has no fluctuation change, and the Pearson correlation coefficient is directly assigned to 0, avoiding the calculation anomaly of the denominator being 0, and ensuring the stable operation of the algorithm.

[0058] Calculate the first one respectively The Euclidean distances between the local renal pelvic pressure sequence at time 1 and the local renal pelvic pressure sequences at all other time points are calculated, and the minimum value among all Euclidean distances is selected. complement normalized value Defined as the first Consistency of local renal pelvis pressure at any given time.

[0059] in, , It represents the maximum Euclidean distance between any two pairs of local renal pelvic pressure sequences at all times in the renal pelvic pressure time series.

[0060] Preferably, in some possible implementations of the embodiments of the present invention, the first... Repeatability of local renal pelvis pressure change trend at time t = 1 Mean value of local renal pelvis pressure consistency at time t = 0 complement , denoted as the The degree of disorder in the local periodic pattern corresponding to a given moment.

[0061] It should be noted that under normal steady-state conditions, the local sequence similarity of renal pelvic pressure at different times is high, with large Pearson correlation coefficients and small Euclidean distances. Such fluctuations are considered normal and compliant periodic fluctuations. However, when abnormal conditions such as tubing blockage, poor drainage, or flow mismatch occur, the original periodic pattern of pressure is disrupted, local sequence similarity decreases significantly, Pearson correlation coefficients decrease, and Euclidean distances increase. The smaller the value, the more likely this type of fluctuation is to be an abrupt and abnormal fluctuation with irregular sharp rises and falls.

[0062] Preferably, in some possible implementations of the embodiments of the present invention, in the injection pump flow rate time sequence, the first... The infusion pump flow rates of all target times corresponding to time point are arranged in chronological order to form the first... The local sequence of injection pump flow rates at each given time.

[0063] In the time series of flow rates of the negative pressure suction pump, the first... The negative pressure suction pump flow rates corresponding to all target times are arranged in chronological order to form the first... The local sequence of flow rates of the negative pressure suction pump at each moment.

[0064] Calculate the first The local sequence of injection pump flow rate at time t and the first The Pearson correlation coefficient of the local sequence of the negative pressure suction pump flow rate at time t is denoted as the nth time. Positive synergy between inflow and outflow of traffic at any given moment.

[0065] In the In the local sequence of the injection pump flow rate and the local sequence of the negative pressure suction pump flow rate corresponding to the time, the difference between the injection pump flow rate and the negative pressure suction pump flow rate is calculated one by one according to the same ordinal position, and the flow difference sequence is constructed by combining them in sequence.

[0066] Obtain the flow difference sequence and the first The Pearson correlation coefficient of the local sequence of renal pelvis pressure at time t is denoted as the t-th time. The correlation coefficient between flow rate difference and pressure at any given time.

[0067] Preferably, in some possible implementations of the embodiments of the present invention, the first... Positive synergy of inbound and outbound traffic at any given moment The opposite number , then obtain With the Mean value of the correlation coefficient between flow rate difference and pressure at time point ,Will normalized value , denoted as the Effective inducibility of pressure fluctuations at any given moment.

[0068] It should be noted that when the flow rate of the perfusion pump and the flow rate of the negative pressure suction pump change in a positive and coordinated manner, the inflow and outflow of fluid in the renal pelvis can maintain a dynamic match, the overall fluid balance is maintained, and the renal pelvis pressure remains in a stable equilibrium. Under this condition, the pressure is not driven by the imbalance of inflow and outflow, and the pressure fluctuation is not caused by flow imbalance. Therefore, such fluctuations are more likely to be invalid interference fluctuations caused by acquisition noise or slight physiological disturbances. Conversely, the smaller the positive coordination between inflow and outflow, the more likely it is to be a dangerous abnormal fluctuation caused by flow imbalance. Furthermore, the greater the flow difference between the infusion pump and the negative pressure suction pump, i.e. the greater the net inflow, the more fluid accumulates in the renal pelvis, and the higher the intraluminal pressure. The two are naturally positively correlated. Therefore, the larger the correlation coefficient between flow difference and pressure, the more the pressure fluctuation is driven directly by the imbalance of inflow and outflow and the violent fluctuation of net inflow. This is a typical cause of dangerous fluctuations caused by flow imbalance and requires regulatory intervention. Conversely, the smaller the correlation coefficient between flow difference and pressure, the more the pressure fluctuation is not affected by fluid inflow and outflow and is mostly caused by non-operating factors such as random disturbances and sensor noise. There is no substantial cause of dangerous fluctuations.

[0069] Preferably, in some possible implementations of the embodiments of the present invention, the first... The degree of disorder of the local periodic regularity at time t and the first The mean of the effective inducibility of pressure fluctuation at time t is denoted as the th. The effective risk fluctuation factor of the pressure at any given time.

[0070] Preferably, in some possible implementations of the embodiments of the present invention, the calculation of the first... The sum of the effective risk fluctuation factor and the preset constant corresponding to the pressure at time t, and the sum of this value and the first... The product of the severity of the suspected problem at time t is denoted as the i-th. The weight of renal pelvis pressure control at any time.

[0071] This invention is now complete.

[0072] In summary, in this embodiment of the invention, the severity of the suspected problem at each moment is determined based on the renal pelvis pressure magnitude in the renal pelvis pressure time series and the differences in renal pelvis pressure at adjacent moments. Based on the differences in renal pelvis pressure within different time periods in the renal pelvis pressure time series, and the correlation of data changes in the renal pelvis pressure time series, the infusion pump flow rate time series, and the negative pressure suction pump flow rate time series within the same time period, the effective pressure risk fluctuation factor corresponding to each moment is determined, thereby determining the renal pelvis pressure control weight for each moment. This weighted time series is then constructed. The renal pelvis pressure time series and the time-weighted time series are input into a fuzzy controller, which outputs infusion pump flow rate adjustment commands and negative pressure suction pump flow rate adjustment commands, achieving stable control of renal pelvis pressure. This invention, by introducing time weights, enhances the ability to identify the changing trends of renal pelvis pressure data during critical periods, improves system response speed, dynamic stability, and safety protection performance, thereby maintaining constant renal pelvis pressure more accurately and robustly.

[0073] 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction, characterized in that, The system includes: Sensing and Acquisition Layer: Used to acquire time-series sequences of renal pelvis pressure, perfusion pump flow rate, and negative pressure suction pump flow rate; Data processing layer: used to determine the severity of suspected problems at each time step based on the renal pelvis pressure magnitude in the renal pelvis pressure time series and the renal pelvis pressure differences between adjacent time steps; Based on the differences in renal pelvis pressure at different time points in the renal pelvis pressure time series, the degree of local periodic regularity disorder corresponding to each time point is determined; based on the correlation of data changes in the renal pelvis pressure time series, infusion pump flow time series, and negative pressure suction pump flow time series within the same time period, the degree of effective inducing pressure fluctuation corresponding to each time point is determined; based on the magnitude of the degree of local periodic regularity disorder and the degree of effective inducing pressure fluctuation, the effective risk fluctuation factor of pressure corresponding to each time point is determined. The severity of the suspected problem is adjusted based on the pressure effective hazard fluctuation factor to determine the renal pelvis pressure control weight at each moment; the renal pelvis pressure control weights at all moments are arranged in chronological order to form a time-weighted time series. Intelligent decision layer: used to input the renal pelvis pressure time series and time-weighted time series into the fuzzy controller, and output the perfusion pump flow rate adjustment command and the negative pressure suction pump flow rate adjustment command; Execution driver layer: Used to receive adjustment commands and dynamically adjust the operating parameters of the infusion pump and the negative pressure suction pump to achieve stable control of renal pelvis pressure.

2. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 1, characterized in that, The specific steps for determining the severity of a suspected problem at each moment are as follows: In the renal pelvis pressure time series, the absolute value of the difference between the renal pelvis pressure and the preset target renal pelvis pressure at each moment is calculated, and the ratio of the absolute value of the difference to the preset target renal pelvis pressure is recorded as the pressure deviation at each moment. The moment when the pressure deviation exceeds the preset deviation threshold is defined as the pressure deviation moment, and the pressure deviation period is composed of adjacent pressure deviation moments. If each moment is not a pressure deviation moment, then the pressure deviation degree at each moment is recorded as the pressure deviation performance at each moment. If each moment is a pressure deviation moment, calculate the sum of the normalized value of the length of the pressure deviation period to which each moment belongs and a preset constant, and multiply the sum by the pressure deviation degree at each moment to record the pressure deviation performance at each moment. Based on the difference in renal pelvis pressure between adjacent moments in the renal pelvis pressure time series, determine the degree of sudden change in renal pelvis pressure at each moment; The severity of the suspected problem at each moment is determined based on the observed pressure deviation and the magnitude of the sudden change in renal pelvis pressure.

3. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 2, characterized in that, The specific steps for determining the degree of sudden change in renal pelvis pressure at each moment are as follows: Select the first few preset time range thresholds that are closest to each time interval, and combine them with each time interval to form the target time corresponding to each time interval. The mean of the absolute values ​​of the differences in renal pelvis pressure between all adjacent target times is recorded as the degree of sudden change in renal pelvis pressure corresponding to each time interval.

4. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 2, characterized in that, The specific steps for determining the severity of the suspected problem at each moment based on the pressure deviation and the magnitude of the sudden change in renal pelvis pressure include the following: The mean of the normalized values ​​of the pressure deviation from the performance at each moment and the normalized values ​​of the sudden change in renal pelvis pressure is recorded as the suspected problem severity at each moment.

5. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 1, characterized in that, The specific steps for determining the local periodicity disorder at each moment are as follows: Select the first few preset time range thresholds that are closest to the time interval of each time moment, and combine them with each time moment to refer to the target time moment corresponding to each time moment; In the renal pelvis pressure time series, the renal pelvis pressure at all target times is arranged in chronological order to form the local renal pelvis pressure sequence corresponding to each time point; Calculate the Pearson correlation coefficient between the local renal pelvis pressure sequence at each time point and the local renal pelvis pressure sequences at other time points. Select the maximum value among all Pearson correlation coefficient values ​​and define the normalized value of the maximum value as the repeatability of the local renal pelvis pressure change trend at each time point. Calculate the Euclidean distance between the local renal pelvis pressure sequence at each time step and the local renal pelvis pressure sequences at other time steps. Select the minimum value among all Euclidean distances and define the complement normalized value of the minimum value as the local renal pelvis pressure consistency at each time step. The degree of disorder of the local periodic regularity at each moment is determined based on the repeatability of the local renal pelvis pressure change trend and the consistency of the local renal pelvis pressure.

6. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 5, characterized in that, The specific steps for determining the degree of local periodic regularity disorder at each moment based on the repeatability of the local renal pelvis pressure change trend and the consistency of the local renal pelvis pressure are as follows: The complement of the mean of the repeatability of the local renal pelvis pressure change trend and the consistency of the local renal pelvis pressure at each time moment is denoted as the local periodic regularity disorder at each time moment.

7. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 5, characterized in that, The specific steps for determining the effective causative factor degree of pressure fluctuation at each moment are as follows: In the injection pump flow time series, the injection pump flow rates at all target times are arranged in chronological order to form a local sequence of injection pump flow rates at each time point. In the time series of negative pressure suction pump flow rate, the negative pressure suction pump flow rate at all target times is arranged in chronological order to form a local sequence of negative pressure suction pump flow rate at each time. Calculate the Pearson correlation coefficient between the local sequence of the injection pump flow rate and the local sequence of the negative pressure suction pump flow rate at each time point, and denote it as the positive synergy of the flow inflow and outflow at each time point; In the local sequence of the injection pump flow rate and the local sequence of the negative pressure suction pump flow rate at each moment, the difference between the injection pump flow rate and the negative pressure suction pump flow rate is calculated one by one according to the same ordinal position, and the flow difference sequence is constructed by combining them in sequence. The Pearson correlation coefficient between the flow difference sequence and the local renal pelvis pressure sequence at each time step is obtained and denoted as the flow difference pressure coupling correlation coefficient at each time step. Based on the magnitude of the correlation coefficient between the positive synergy of flow inflow and outflow and the pressure coupling of flow difference, the effective inducing factor of pressure fluctuation at each moment is determined.

8. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 7, characterized in that, The specific steps for determining the effective cause of pressure fluctuation at each moment based on the magnitude of the correlation coefficient between the positive synergy of flow inflow and outflow and the pressure coupling of flow difference are as follows: The normalized value of the mean of the negative number of the positive synergy between flow inflow and outflow at each time point and the correlation coefficient between flow difference and pressure is denoted as the effective inducing factor degree of pressure fluctuation at each time point.

9. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 1, characterized in that, The specific steps for determining the effective risk fluctuation factor of pressure at each moment are as follows: The average of the local periodic regularity disorder and the effective inducing factor of pressure fluctuation at each moment is denoted as the effective risk fluctuation factor of pressure at each moment.

10. The renal pelvis pressure stabilization control system integrating perfusion and negative pressure suction according to claim 1, characterized in that, The specific steps for determining the renal pelvis pressure control weight at each moment are as follows: Calculate the sum of the effective risk fluctuation factor and the preset constant corresponding to each moment, and multiply the sum by the severity of the suspected problem at each moment, and record it as the renal pelvis pressure control weight at each moment.

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