A bladder pressure information monitoring method, urinary catheter and storage medium

Abstract

The present invention relates to the technical field of bladder pressure monitoring, and in particular to a bladder pressure information monitoring method, a urinary catheter and a storage medium. The invention obtains corrected urine flow data at each moment; obtains the normal urine volume influence probability of each bladder pressure data point based on the urine flow data and bladder pressure data at each moment, the maximum bladder pressure data affected by urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data; obtains effective bladder pressure data, adjusts the bladder pressure data of the effective bladder pressure data point, and obtains corrected bladder pressure data for each effective bladder pressure data point; and then obtains a urine volume-bladder pressure curve. The present invention obtains a more accurate urine volume-bladder pressure curve by correcting the urine volume data and the bladder pressure data, thereby improving the effect of bladder status diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of bladder pressure monitoring, and in particular to a bladder pressure information monitoring method, a urinary catheter and a storage medium. Background Art

[0002] The bladder is an organ in the human body and a part of the urinary system. Its main function is to store urine and empty it at the appropriate time. The pressure changes in the bladder can reflect the functional status of the bladder during urination. The patient's bladder status is analyzed by the amount of urine excreted during a urination process, the corresponding maximum bladder pressure, and the pressure trend when excreting the same amount of urine.

[0003] In the prior art, the urine volume curve is obtained by integrating the urine flow obtained by the uroflowmeter and the bladder pressure curve is obtained by the bladder pressure tester, and then the data are directly fitted to obtain the urine volume-bladder pressure curve under the influence of urine volume; however, since the catheter may not necessarily inject all the urine into the bladder, the urine may flow along the tube wall, causing the urine flow curve obtained by the uroflowmeter to be inaccurate, and the bladder pressure curve has not only a protruding state caused by coughing, but also a fluctuating state caused by breathing, resulting in the analyzed urine volume-bladder pressure curve cannot accurately indicate the changes in bladder pressure affected by urine volume, and cannot accurately analyze the bladder status. Summary of the invention

[0004] In order to solve the technical problem that the urine volume-bladder pressure curve cannot accurately indicate the change of bladder pressure affected by urine volume, the purpose of the present invention is to provide a bladder pressure information monitoring method, a urinary catheter and a storage medium. The technical scheme adopted is as follows:

[0005] The present invention provides a method for monitoring bladder pressure information, the method comprising:

[0006] Obtain the patient's urine flow curve and bladder pressure curve;

[0007] According to the change trend of urine flow data points in the neighborhood of each urine flow data point on the urine flow curve, the initial urination point on the urine flow curve is screened out; according to the urine flow data in the neighborhood at the time corresponding to the initial urination point, the urine flow data is adjusted to obtain the corrected urine flow data at each time;

[0008] According to the bladder pressure data of each target bladder pressure data point and the degree of change fluctuation of the bladder pressure data points in the corresponding neighborhood, the maximum bladder pressure data affected by the urine volume is screened out, the target bladder pressure data point being the bladder pressure data point at a time greater than or equal to the time corresponding to the initial urination point on the bladder pressure curve; according to the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by the urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained;

[0009] The bladder pressure data of the effective bladder pressure data point is adjusted according to the normal urine volume influence probability to obtain the corrected bladder pressure data of each effective bladder pressure data point; the effective bladder pressure data point is a bladder pressure data point whose normal urine volume influence probability is greater than a set effective threshold value;

[0010] A urine volume-bladder pressure curve is obtained based on the corrected urine flow data and the corrected bladder pressure data at the same time.

[0011] Furthermore, the method for obtaining the initial urination point includes:

[0012] Obtaining the initial urination point probability of each urine flow data point according to the change trend of the urine flow data point within the neighborhood of each urine flow data point on the urine flow curve;

[0013] The urine flow data point with the largest initial urination point probability among all urine flow data points is selected as the initial urination point.

[0014] Furthermore, the method for obtaining the initial urination point probability includes:

[0015] Calculate the difference between the urine flow data of each urine flow data point and the urine flow data of other urine flow data points within the corresponding neighborhood as the first difference;

[0016] The sum of the first differences between each urine flow data point and all other subsequent urine flow data points is calculated as the first change feature; the sum of the first differences between each urine flow data point and all other previous urine flow data points is calculated as the second change feature; according to the difference between the first change feature and the second change feature, the initial urination point probability of each urine flow data point is obtained.

[0017] Furthermore, the method for obtaining the corrected urine flow data includes:

[0018] Calculate the mean of all urine flow data within the neighborhood at the time corresponding to the initial urination point to obtain the urine flow data error;

[0019] The difference between the urine flow data at each moment and the urine flow data error is calculated to obtain the corrected urine flow data at each moment.

[0020] Furthermore, the method for obtaining the maximum bladder pressure data affected by urine volume includes:

[0021] For any target bladder pressure data point, the absolute value of the slope between the target bladder pressure data point and each other bladder pressure data point within the corresponding neighborhood is calculated, and the fluctuation degree of the absolute value of the slope is analyzed as the fluctuation degree of the change;

[0022] According to the bladder pressure data of each target bladder pressure data point and the corresponding variation fluctuation degree, the influence degree of urine volume on each target bladder pressure data point is obtained. The bladder pressure data is positively correlated with the influence degree of urine volume, and the variation fluctuation degree is negatively correlated with the influence degree of urine volume.

[0023] The bladder pressure data of the target bladder pressure data point that is most affected by the urine volume is used as the maximum bladder pressure data affected by the urine volume.

[0024] Furthermore, the method for obtaining the normal urine volume influence probability includes:

[0025] The bladder pressure data at each moment is normalized using the maximum bladder pressure data and the minimum bladder pressure data affected by the urine volume to obtain a first normalized value at each moment;

[0026] The urine flow data at each moment is normalized using the maximum urine flow data and the minimum urine flow data to obtain a second normalized value at each moment;

[0027] According to the difference between the first normalized value and the second normalized value at each moment, the normal urine volume influence probability of each bladder pressure data point is obtained, and the difference between the first normalized value and the second normalized value is negatively correlated with the normal urine volume influence probability.

[0028] Furthermore, the method for obtaining the corrected bladder pressure data includes:

[0029] Determining whether it is necessary to adjust the bladder pressure data of each bladder pressure data point according to the normal urine volume influence probability of all bladder pressure data points;

[0030] If adjustment is required, for any valid bladder pressure data point, the next valid bladder pressure data point of the valid bladder pressure data point is taken as a related point, and the cumulative value of the probability of normal urine volume between the valid bladder pressure data point and its related point is calculated;

[0031] The ratio of the normal urine volume probability of the effective bladder pressure data point to the accumulated value is used as the weight of the effective bladder pressure data point; the ratio of the normal urine volume probability of the relevant point to the accumulated value is used as the weight of the relevant point;

[0032] Corrected bladder pressure data of the effective bladder pressure data point is obtained by combining the effective bladder pressure data point with the weights of the relevant points and the corresponding bladder pressure data.

[0033] Further, judging whether it is necessary to adjust the bladder pressure data of each bladder pressure data point according to the normal urine volume influence probability of all bladder pressure data points includes:

[0034] The probability of normal urine volume of all bladder pressure data points is averaged. If the average is greater than the preset relevant threshold, adjustment is required, otherwise no adjustment is required.

[0035] The present invention also proposes a urinary catheter, which comprises a urine volume-bladder pressure curve monitoring device, and the urine volume-bladder pressure curve monitoring device comprises a data acquisition module, a urine flow correction module, a bladder pressure correction module, and a urine volume-bladder pressure curve monitoring module:

[0036] Data acquisition module: obtain the patient's urine flow curve and bladder pressure curve;

[0037] Urine flow correction module: according to the change trend of urine flow data points in the neighborhood of each urine flow data point on the urine flow curve, the initial urination point on the urine flow curve is screened out; according to the urine flow data in the neighborhood at the time corresponding to the initial urination point, the urine flow data is adjusted to obtain the corrected urine flow data at each time;

[0038] Bladder pressure correction module: according to the bladder pressure data of each target bladder pressure data point and the degree of change and fluctuation of the bladder pressure data points within the corresponding neighborhood, the maximum bladder pressure data affected by urine volume is screened out, and the target bladder pressure data point is the bladder pressure data point at a time greater than or equal to the time corresponding to the initial urination point on the bladder pressure curve; according to the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained; according to the normal urine volume influence probability, the bladder pressure data of the effective bladder pressure data point is adjusted to obtain the corrected bladder pressure data of each effective bladder pressure data point; the effective bladder pressure data point is the bladder pressure data point at which the normal urine volume influence probability is greater than the set effective threshold;

[0039] Urine volume-bladder pressure curve monitoring module: obtains the urine volume-bladder pressure curve according to the corrected urine flow data and corrected bladder pressure data at the same time.

[0040] The present invention also provides a storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the bladder pressure information monitoring method described above are implemented.

[0041] The present invention has the following beneficial effects:

[0042] The present invention selects the initial urination point on the urine flow curve according to the change trend of the urine flow data in the neighborhood range of the urine flow data at each moment on the urine flow curve, and by analyzing the change trend of the urine flow data, it can be determined that the most likely initial urination point; the urine flow data is adjusted according to the urine flow data in the neighborhood range at the time corresponding to the initial urination point to obtain the corrected urine flow data at each moment, and by adjusting the urine flow data at each moment, possible noise or error is eliminated to make it more in line with the actual urination pattern; during the urination process, the change in bladder pressure is not only related to the urine volume, but may also be affected by other factors. In order to analyze the impact of urine volume on bladder pressure, according to the bladder pressure data of each target bladder pressure data point and the degree of change and fluctuation of the bladder pressure data point in the corresponding neighborhood range, the maximum bladder pressure data affected by urine volume is selected, which helps to more accurately understand the relationship between urine volume and bladder pressure. The target bladder pressure data point is the bladder pressure data point on the bladder pressure curve. The bladder pressure data point at a time greater than or equal to the time corresponding to the initial urination point; according to the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained, and the degree to which the bladder pressure data is affected by urine volume is evaluated; the bladder pressure data of the effective bladder pressure data point is adjusted according to the normal urine volume influence probability, and the corrected bladder pressure data of each effective bladder pressure data point is obtained, and the bladder pressure fluctuation caused by non-urine volume factors is eliminated or reduced, so that the bladder pressure data can better reflect the true state of the bladder; the effective bladder pressure data point is the bladder pressure data point whose normal urine volume influence probability is greater than the set effective threshold; according to the corrected urine flow data and the corrected bladder pressure data at the same time, the urine volume-bladder pressure curve is obtained, which provides intuitive information about the patient's bladder function and helps to better analyze the patient's bladder function status. The present invention obtains a more accurate urine volume-bladder pressure curve by correcting the urine volume data and the bladder pressure data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of a method for monitoring bladder pressure information provided by one embodiment of the present invention;

[0045] Figure 2 A flow chart of a method for obtaining maximum bladder pressure data affected by urine volume provided by one embodiment of the present invention;

[0046] Figure 3 This is a structural block diagram of a urine volume-bladder pressure curve monitoring device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a bladder pressure information monitoring method, a urinary catheter and a storage medium proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, 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 belongs.

[0049] The following is a detailed description of a bladder pressure information monitoring method, a urinary catheter and a storage medium provided by the present invention in conjunction with the accompanying drawings.

[0050] See also Figure 1 , which shows a flow chart of a bladder pressure information monitoring method provided by an embodiment of the present invention, and the specific method includes:

[0051] Step S1: Obtain the patient's urine flow curve and bladder pressure curve.

[0052] During the process of bladder pressure monitoring, a catheter is inserted into the patient's bladder, and physiological saline is injected through the catheter to simulate the process of urine volume change in the bladder, so as to measure the pressure change of the bladder under different urine volume states. In the embodiment of the present invention, considering that the water flow may flow into the urinary flowmeter evenly along the wall of the catheter during the process of injecting water into the bladder, causing leakage, resulting in the situation that the urine flow data monitored by the urinary flowmeter is too large, and the urine volume-bladder pressure curve cannot accurately indicate the bladder pressure change affected by the urine volume. It is necessary to correct and analyze the urine flow data to more accurately describe the urine flow and the impact on the bladder pressure during urination; first, inject an appropriate amount of physiological saline at a uniform speed to monitor the bladder pressure, the pressure sensor is embedded in the inner end of the catheter to monitor the pressure in the bladder, and the urinary flowmeter monitors the urine flow data discharged by the catheter; obtain the patient's urine flow curve and bladder pressure curve. Each data point on the curve is composed of each moment and corresponding data.

[0053] It should be noted that, in one embodiment of the present invention, the interval between moments is 1 second; in other embodiments of the present invention, the size of the interval between moments can be set according to specific circumstances, which is not limited or elaborated here.

[0054] It should be noted that the horizontal axis of the urine flow curve is the collection time, and the vertical axis is the urine flow data. The urine flow data reflects the volume of urine discharged at each moment; the horizontal axis of the bladder pressure curve is the collection time, and the vertical axis is the bladder pressure data. The bladder pressure data reflects the pressure on the bladder wall during the bladder filling process affected by the urine volume.

[0055] Step S2: According to the change trend of urine flow data points in the neighborhood of each urine flow data point on the urine flow curve, the initial urination point on the urine flow curve is screened out; according to the urine flow data in the neighborhood at the time corresponding to the initial urination point, the urine flow data is adjusted to obtain the corrected urine flow data at each time.

[0056] For the human body, before urination, the urine flow data is theoretically zero, but both water injection and urination are performed in the catheter. It is inevitable that during the water injection process, the water will drip from the catheter wall due to gravity, resulting in urine flow data before urination. Because the water injection before urination is uniform, the change in urine flow data caused by the dripping of water injection is also unchanged, and the data is relatively stable; on the contrary, if the urine flow data increases after urination begins, the fluctuation of urine flow data in the neighborhood of the urine flow data at the corresponding moment is greater than that before urination begins; since there is a large difference in the change of urine flow data before and after urination, the initial urination point on the urine flow curve is screened out according to the change trend of the urine flow data points in the neighborhood of each urine flow data point on the urine flow curve.

[0057] Preferably, in one embodiment of the present invention, the method for obtaining the initial urination point includes:

[0058] According to the change trend of the urine flow data points within the neighborhood of each urine flow data point on the urine flow curve, the initial urination point probability of each urine flow data point is obtained;

[0059] The initial urination point probability is used to describe the possibility of the patient having an initial urination point under specific conditions. The higher the initial urination point probability, the higher the possibility. The largest initial urination point probability among the urine flow data at all times is selected, and the corresponding urine flow data point is taken as the initial urination point.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the probability of the initial urination point includes:

[0061] Calculate the difference between the urine flow data of each urine flow data point and the urine flow data of other urine flow data points within the corresponding neighborhood as the first difference;

[0062] The sum of the first differences between each urine flow data point and all other subsequent urine flow data points is calculated as the first change feature; the sum of the first differences between each urine flow data point and all other previous urine flow data points is calculated as the second change feature; according to the difference between the first change feature and the second change feature, the initial urination point probability of each urine flow data point is obtained.

[0063] In one embodiment of the present invention, the formula for the initial urination point probability is expressed as:

[0064] ;

[0065] in, Indicates The probability of the initial urination point for each urine flow data point; Indicates Urine flow data corresponding to urine flow data points; Indicates the neighborhood The first urine flow data point after Urine flow data corresponding to urine flow data points; Indicates the neighborhood The urine flow data point before Urine flow data corresponding to urine flow data points; represents a hyperparameter; Represents the corresponding number before and after the urine flow data point within the neighborhood.

[0066] In the formula for the probability of the initial urination point, Indicates the calculation The difference between the urine flow data points corresponding to the urine flow data points after the urine flow data point is the first change feature. The larger the first change feature, the After the urine flow data point The greater the difference between the urine flow data corresponding to the data points, the greater the change between the data points; Indicates the calculation The cumulative sum of the differences between the urine flow data points and the previous urine flow data points corresponding to the urine flow data is the second change feature. The larger the second change feature, the The urine flow data point before The greater the difference between the urine flow data corresponding to the data points, the The greater the change between the data points before the first urine flow data point, the less likely it is the initial urination point; on the contrary, The smaller the change between the data points before the first urine flow data point, the closer the second change feature is to 0, which indicates that it is basically stable. The smaller the change in the data point before the urine flow data point, the The greater the change in the data point after a urine flow data point, the closer it is to the initial urination point, and the greater the probability of the initial urination point.

[0067] It should be noted that, in one embodiment of the present invention, the neighborhood range corresponding to the urine flow data point is a range centered on the urine flow data point and consisting of the same number of data points before and after the central urine flow data point.

[0068] It should be noted that, in one embodiment of the present invention, by To indicate the difference between the first change feature and the second change feature, a correlation relationship is established that the larger the first change feature, the smaller the second change feature, the greater the difference between the first accumulation and the second accumulation, and the greater the probability of the initial urination point; in other embodiments of the present invention, the correlation relationship that the larger the first change feature, the smaller the second change feature, the greater the difference between the first change feature and the second change feature, and the greater the probability of the initial urination point can also be achieved according to the subtraction method, that is, The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0069] It should be noted that in order to make the formula meaningful and avoid the situation where the denominator of the formula is 0, a hyperparameter is set. The hyperparameter is a positive number close to zero but not equal to zero, such as 0.01. The implementers can set it according to the specific situation. The specific means are technical means well known to technical personnel in this field and will not be elaborated here.

[0070] The initial urination point is usually the beginning of the urination process and can be used as a benchmark or reference point for subsequent analysis. By analyzing the urine flow data in the neighborhood of the initial urination point, possible measurement errors or outliers can be identified and calibrated to make subsequent data more accurate and reliable. Therefore, the urine flow data is adjusted according to the urine flow data in the neighborhood of the initial urination point to obtain the corrected urine flow data at each moment.

[0071] Preferably, in one embodiment of the present invention, the method for obtaining the corrected urine flow data includes:

[0072] Calculate the mean of all urine flow data within the neighborhood at the time corresponding to the initial urination point to obtain the urine flow data error;

[0073] The difference between the urine flow data at each moment and the urine flow data error is calculated to obtain the corrected urine flow data at each moment.

[0074] It should be noted that, in one embodiment of the present invention, since the urine flow data before the initial urination point is caused by water leaking along the catheter during the uniform water injection process, it should be the corresponding urine flow error; the time neighborhood range is the range composed of all times before the corresponding time of the initial urination point.

[0075] Step S3: According to the bladder pressure data of each target bladder pressure data point and the degree of fluctuation of the bladder pressure data points in the corresponding neighborhood, the maximum bladder pressure data affected by the urine volume is screened out, and the target bladder pressure data point is the bladder pressure data point on the bladder pressure curve at a time greater than or equal to the time corresponding to the initial urination point; according to the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by the urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained.

[0076] Changes in bladder pressure are not only related to urine volume, but may also be affected by other factors. In order to avoid the influence of peak points caused by behaviors such as coughing, obtaining changes in bladder pressure caused by urine volume will help evaluate the true condition of the bladder; under normal circumstances, an increase in urine flow will lead to increased pressure in the bladder; in order to analyze the condition of the bladder, by analyzing the maximum bladder pressure data affected by urine volume, the maximum tolerance of the bladder under the influence of urine volume can be evaluated; by analyzing the probability of the influence of normal urine volume, the relationship between bladder function and urine flow can be more accurately evaluated.

[0077] During the urination process, that is, after the initial urination point, the analysis can reach the maximum bladder pressure data affected by urine volume, which will have reference value; considering the degree of change and fluctuation within the neighborhood of the target bladder pressure data point is to capture the stability or trend of the bladder pressure change near this point, reflecting the stability of the bladder to the increase in urine volume; by combining the bladder pressure data and the degree of change and fluctuation within the neighborhood, data points with both high bladder pressure data and greater stability of the bladder to the increase in urine volume can be screened out. These points are most likely to represent the moment most affected by urine volume. Therefore, according to the bladder pressure data of each target bladder pressure data point and the degree of change and fluctuation of the bladder pressure data point within the corresponding neighborhood, the maximum bladder pressure data affected by urine volume is screened out. The target bladder pressure data point is the bladder pressure data point on the bladder pressure curve at a time greater than or equal to the time corresponding to the initial urination point.

[0078] Preferably, in one embodiment of the present invention, the method for obtaining the maximum bladder pressure data affected by urine volume can be found in Figure 2 , which shows a flow chart of a method for obtaining maximum bladder pressure data affected by urine volume provided by an embodiment of the present invention, the method comprising:

[0079] Step S201: For any target bladder pressure data point, the absolute value of the slope between the target bladder pressure data point and each other bladder pressure data point in the corresponding neighborhood is calculated, and the fluctuation degree of the absolute value of the slope is analyzed as the variation fluctuation degree.

[0080] Since bladder pressure is a dynamic parameter that changes with time, selecting bladder pressure data points within a neighborhood range can focus on analyzing local changes near the target bladder pressure data points, which helps to detect abnormal fluctuations or mutations in the local range; by calculating the absolute value of the slope, this dynamic change can be captured to understand the changing trend of bladder pressure at different time points; the fluctuation degree of the absolute value of the slope indicates the fluctuation of the changing trend of bladder pressure at different time points, which is used to evaluate the severity of the change of bladder pressure within the neighborhood range.

[0081] It should be noted that the absolute value of the slope between the target bladder pressure data point and each other bladder pressure data point within the corresponding neighborhood is calculated as the ratio of the difference between the ordinate bladder pressure data and the difference between the abscissa moments; in one embodiment of the present invention, the degree of fluctuation can be indicated by calculating the variance of the absolute value of the slope. The larger the variance, the greater the degree of fluctuation. The specific means are well known to those skilled in the art and will not be elaborated here.

[0082] Step S202: According to the bladder pressure data of each target bladder pressure data point and the corresponding variation fluctuation degree, the influence degree of urine volume on each target bladder pressure data point is obtained, wherein the bladder pressure data is positively correlated with the influence degree on urine volume, and the variation fluctuation degree is negatively correlated with the influence degree on urine volume.

[0083] Bladder pressure increases with the increase of urine volume. When urine gradually accumulates in the bladder, the pressure on the bladder wall will also increase accordingly. The degree of change fluctuation evaluates the drastic degree of change of bladder pressure within the neighborhood. When the degree of change fluctuation is small, it means that the bladder pressure is steadily increasing, usually corresponding to the gradual increase of urine volume. However, when the degree of change fluctuation is large, it may mean that the bladder pressure has changed dramatically within the neighborhood. It may be caused by a sudden increase in urine volume caused by coughing, abnormal contraction of bladder muscles or other factors, and is not normally affected by urine volume. Therefore, the bladder pressure data is positively correlated with the degree of influence of urine volume, and the degree of change fluctuation is negatively correlated with the degree of influence of urine volume. Among them, the positive correlation relationship indicates that the dependent variable will increase with the increase of the independent variable, and the dependent variable will decrease with the decrease of the independent variable. The specific relationship can be a multiplication relationship, an addition relationship, and the power of an exponential function, which is determined by actual application; the negative correlation relationship indicates that the dependent variable will decrease with the increase of the independent variable, and the dependent variable will increase with the decrease of the independent variable. It can be a subtraction relationship, a division relationship, etc., which is determined by actual application.

[0084] In one embodiment of the present invention, the formula for the degree of influence of urine volume is:

[0085] ;

[0086] ;

[0087] in, Indicates The degree to which each bladder pressure data point is affected; Indicates the judgment Whether a bladder pressure data point is a parameter of a target bladder pressure data point; Indicates bladder pressure data points; Indicates The bladder pressure data point is within the neighborhood of bladder pressure data for other bladder pressure data points; Indicates The bladder pressure data point corresponds to the moment; Indicates The bladder pressure data point is within the neighborhood of Other bladder pressure data points correspond to the time; Indicates the time corresponding to the initial urination point; represents the number of bladder pressure data points within the neighborhood of the bladder pressure data point; Represents an exponential function with a natural constant as its base.

[0088] In the formula for the degree of influence of urine volume, if The bladder pressure data point corresponds to the time Greater than or equal to the time corresponding to the initial urination point, The first bladder pressure data point is the target bladder pressure data point, and the parameter is set to 1, otherwise it is set to 0; the target bladder pressure data point is analyzed, and the exponential function with the natural constant as the base is used to convert Perform negative correlation mapping, It means to calculate the absolute value of the slope between the target bladder pressure data point and each other bladder pressure data point in the corresponding neighborhood. It indicates the degree of fluctuation of the absolute value of the slope by calculating the variance analysis of the absolute value of the slope, that is, the degree of fluctuation of the change. The larger the variance, the greater the degree of fluctuation, which means that the degree of fluctuation of the change between data points is greater and the degree affected by urine volume is smaller. On the contrary, the smaller the variance, the smaller the degree of fluctuation, which means that the degree of fluctuation of the change between data points is smaller and the degree affected by urine volume is greater.

[0089] It should be noted that, in other embodiments of the present invention, The method of negative correlation mapping of the function can also be achieved by finding the inverse of the degree of change fluctuation, and constructing a negative correlation relationship in which the smaller the degree of change fluctuation, the greater the degree of influence of urine volume; the specific means are technical means well known to technical personnel in this field and will not be repeated here.

[0090] It should be noted that when realizing a negative correlation, if the inverse is used, it is necessary to add an artificial parameter to the denominator of the fraction, such as taking a value of 0.01, adding the data to 0.01 as the denominator to avoid the situation where the denominator of the fraction is 0.

[0091] Step S203: taking the bladder pressure data of the target bladder pressure data point that is most affected by the urine volume as the maximum bladder pressure data affected by the urine volume.

[0092] The degree of influence of urine volume is used to evaluate the relationship between bladder pressure and urine flow at different times; the point with the greatest influence of urine volume is selected, which represents the situation where the pressure change of bladder pressure due to urine volume is most significant, and the influence of factors such as coughing is eliminated as much as possible.

[0093] The bladder pressure data will change dynamically with the increase of urine volume, so there is a direct correlation between the change of bladder pressure data points and the urine flow data at the corresponding moment; when the bladder begins to store urine and gradually expands, the pressure in the bladder will gradually rise, so based on the maximum and minimum values ​​of urine flow data and bladder pressure data, as well as urine flow data and bladder pressure data at each moment, different aspects of the relationship between bladder function and urine flow can be provided, and a more comprehensive assessment of the change of bladder pressure affected by urine flow can be made. Therefore, based on the urine flow data and bladder pressure data at each moment, the maximum bladder pressure data affected by urine volume, the minimum bladder pressure data, the maximum urine flow data, and the minimum urine flow data, the probability of normal urine volume affecting each bladder pressure data point is obtained.

[0094] Preferably, in one embodiment of the present invention, the method for obtaining the normal urine volume influence probability includes:

[0095] The bladder pressure data at each moment is normalized by using the maximum bladder pressure data and the minimum bladder pressure data affected by the urine volume to obtain a first normalized value at each moment; the urine flow data at each moment is normalized by using the maximum urine flow data and the minimum urine flow data to obtain a second normalized value at each moment;

[0096] According to the difference between the first normalized value and the second normalized value at each moment, the normal urine volume influence probability of each bladder pressure data point is obtained, and the difference between the first normalized value and the second normalized value is negatively correlated with the normal urine volume influence probability. Among them, the negative correlation relationship means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by actual application.

[0097] In one embodiment of the present invention, the formula for the normal urine volume affecting the probability is expressed as:

[0098] ;

[0099] in, Indicates The probability of normal urine output affecting each bladder pressure data point; Indicates the maximum urine flow data; Indicates minimum urine flow data; Indicates the maximum bladder pressure data affected by urine volume; Indicates minimum bladder pressure data; Indicates Urine flow data at the corresponding moment of each bladder pressure data point; Indicates The bladder pressure data at the corresponding moment of each bladder pressure data point; represents an exponential function with a natural constant as base; Indicates finding the absolute value.

[0100] In the formula for the influence of normal urine volume on probability, Perform negative correlation mapping, Indicates the maximum and minimum values ​​obtained by analyzing the data points for the The data at the corresponding moments of the bladder pressure data points are normalized, and the difference between the normalized results is calculated to indicate the changing trends of the bladder pressure data and the urine flow data. The larger the difference, the greater the difference between the proportion of the urine flow data at the corresponding moment of the bladder pressure data point relative to the maximum and minimum values ​​in the urine flow curve and the proportion of the maximum and minimum values ​​in the bladder pressure data relative to the received urine volume, indicating that the changing trends of the bladder pressure data corresponding to the bladder pressure data point and the urine flow data are inconsistent, and the probability of being affected by normal urine volume is smaller; conversely, the smaller the difference, the more consistent the proportion of the urine flow data at the corresponding moment of the bladder pressure data point in the urine flow curve relative to the maximum and minimum values ​​and the proportion of the maximum and minimum values ​​in the bladder pressure data relative to the received urine volume, indicating that the changing trends of the bladder pressure data corresponding to the bladder pressure data point and the urine flow data are more consistent, and the probability of being affected by normal urine volume is greater.

[0101] It should be noted that, in other embodiments of the present invention, The method of negative correlation mapping of the function can also be implemented by finding the inverse of the difference, constructing a negative correlation relationship in which the greater the difference, the smaller the probability of normal urine volume being affected; the specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0102] It should be noted that when realizing a negative correlation, if the inverse is used, it is necessary to add an artificial parameter to the denominator of the fraction, such as a value of 0.01, to avoid the situation where the denominator of the fraction is 0.

[0103] It should be noted that, in one embodiment of the present invention, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data are the maximum and minimum data directly obtained on the curve.

[0104] In another embodiment of the present invention, a method for obtaining maximum urine flow data includes: for any urine flow data point, calculating the absolute value of the slope between the urine flow data point and each other urine flow data point within the corresponding neighborhood, analyzing the fluctuation degree of the absolute value of the slope as the fluctuation degree of the change of the urine flow data; performing negative correlation mapping on the fluctuation degree of the change of the urine flow data, calculating the product of the urine flow data of each urine flow data point and the negative correlation mapping result, obtaining the maximum probability of each urine flow data point, and taking the urine flow data corresponding to the urine flow data point with the largest maximum probability as the maximum urine flow data; a method for obtaining minimum bladder pressure data includes: fitting the optimal straight line by the gradient descent method for the bladder pressure data points before the initial urination point, the optimal straight line is a fitting straight line parallel to the horizontal axis, and the bladder pressure data corresponding to the vertical axis is taken as the minimum bladder pressure data, and the specific gradient descent method is a technical means well known to those skilled in the art and will not be described in detail here.

[0105] Step S4: adjusting the bladder pressure data of the valid bladder pressure data point according to the probability of influence of normal urine volume to obtain the corrected bladder pressure data of each valid bladder pressure data point; the valid bladder pressure data point is the bladder pressure data point whose probability of influence of normal urine volume is greater than the set effective threshold.

[0106] Considering the probability of normal urine volume influence can more accurately reflect the pressure of the bladder under the influence of normal urine volume and evaluate the validity of the bladder pressure data points; by setting the effective threshold, the bladder pressure data points with a higher probability of normal urine volume influence can be screened out, and abnormal data points caused by coughing and other conditions can be excluded; the bladder pressure data of the effective data points are adjusted based on the probability of normal urine volume influence to more accurately evaluate the patient's bladder function status. Therefore, the bladder pressure data of the effective bladder pressure data points are adjusted according to the probability of normal urine volume influence of each bladder pressure data point to obtain the corrected bladder pressure data of each effective bladder pressure data point; the effective bladder pressure data point is the bladder pressure data point whose probability of normal urine volume influence is greater than the set effective threshold.

[0107] Preferably, in one embodiment of the present invention, the method for obtaining the corrected bladder pressure data includes:

[0108] Judging whether the bladder pressure data of each bladder pressure data point needs to be adjusted according to the normal urine volume influence probability of all bladder pressure data points;

[0109] Taking into account that breathing may cause slight ups and downs in bladder pressure, if adjustment is needed, for any valid bladder pressure data point, the next valid bladder pressure data point of the valid bladder pressure data point is taken as the relevant point, and the cumulative value of the probability of normal urine volume between the valid bladder pressure data point and its relevant point is calculated; the ratio of the probability of normal urine volume of the valid bladder pressure data point to the cumulative value is taken as the weight of the valid bladder pressure data point; the ratio of the probability of normal urine volume of the relevant point to the cumulative value is taken as the weight of the relevant point;

[0110] Corrected bladder pressure data of the effective bladder pressure data point is obtained by combining the effective bladder pressure data point with the weights of the relevant points and the corresponding bladder pressure data.

[0111] Preferably, in one embodiment of the present invention, judging whether it is necessary to adjust the bladder pressure data of each bladder pressure data point according to the normal urine volume influence probability of all bladder pressure data points includes:

[0112] The probability of normal urine volume of all bladder pressure data points is averaged. If the average is greater than the preset relevant threshold, adjustment is required, otherwise no adjustment is required.

[0113] In one embodiment of the present invention, the formula for correcting bladder pressure data is:

[0114] ;

[0115] in, Indicates Corrected bladder pressure data of valid bladder pressure data points; Indicates The probability of normal urine output affecting the effective bladder pressure data point; Indicates The next valid bladder pressure data point The normal urine volume affects the probability; Indicates bladder pressure data of valid bladder pressure data points; Indicates The next valid bladder pressure data point Bladder pressure data.

[0116] In the formula for correcting bladder pressure data, represents the cumulative value of the probability of normal urine volume between a valid bladder pressure data point and the next valid bladder pressure data point; Indicates The ratio between the probability of normal urine volume influence and the cumulative value of a valid bladder pressure data point is the weight of the valid bladder pressure data point. The larger the ratio, the greater the weight of the valid bladder pressure data point, and the greater the significance of the corresponding bladder pressure being affected by normal urine flow; Indicates The ratio between the normal urine volume influence probability and the cumulative value of the valid bladder pressure data point, that is, The weight of the valid bladder pressure data, the larger the ratio, the The greater the weight of an effective bladder pressure data, the more significant the influence of normal urine flow on the corresponding bladder pressure is; by weighting the bladder pressure data with different weights, the bladder pressure data with the greatest significance of the influence of normal urine volume is retained as much as possible, and the ups and downs caused by breathing are eliminated.

[0117] It should be noted that in order to avoid the influence of sudden spikes caused by coughing during urination, the effective bladder pressure data points are analyzed. The effective bladder pressure data points are bladder pressure data points whose probability of being affected by normal urine volume is greater than the set effective threshold, and the set effective threshold is 0.8; in one embodiment of the present invention, the preset relevant threshold is 0.7; when the mean is less than the preset relevant threshold, it is directly judged that there is a problem with the bladder, so there is no need to adjust the bladder pressure data; in other embodiments of the present invention, the size of the effective threshold and the preset relevant threshold can be set according to the specific situation, and no limitation or elaboration is made here.

[0118] It should be noted that, in other embodiments of the present invention, The multiplication method can also be constructed through other basic mathematical operations such as addition. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0119] Step S5: Obtaining a urine volume-bladder pressure curve based on the corrected urine flow data and the corrected bladder pressure data at the same time.

[0120] By correcting the data, the accuracy of the data can be improved, so that the corrected urine volume-bladder pressure curve can more accurately reflect the patient's bladder function and urination situation, thereby improving the accuracy of diagnosis; therefore, the urine volume-bladder pressure curve is obtained based on the corrected urine flow data and corrected bladder pressure data at the same time.

[0121] It should be noted that, in one embodiment of the present invention, the corrected urine volume data at each moment is obtained by integrating the corrected urine flow data; the corrected urine volume data and the corrected bladder pressure data at the same moment constitute data points, and all the data points at the same moment are fitted to obtain a urine volume-bladder pressure curve. The abscissa of the urine volume-bladder pressure curve is the urine volume data, and the ordinate is the bladder pressure data.

[0122] It should be noted that, in one embodiment of the present invention, the data points at all the same moments can be fitted using existing fitting methods such as least squares method and polynomial fitting. The specific fitting method is a technical means well known to those skilled in the art and will not be elaborated here.

[0123] It should be noted that the urine volume-bladder pressure curve can also be used to diagnose the subsequent bladder status of the patient: by intuitively seeing the amount of urine discharged during urination and the maximum bladder pressure that the bladder can withstand, it can be compared with the situation obtained in advance from existing experience to judge the health of the patient's bladder; it can also be reflected by judging the trend of the derivative of the urine volume-bladder pressure curve. How fast the bladder pressure changes during urination; for example, when the trend of the derivative is relatively low, the pressure when the bladder urinates the same amount of urine is lower, which may indicate that the bladder muscles are not strong enough and relevant exercises are needed to strengthen the bladder muscles; when the trend of the derivative is relatively high, the pressure when the bladder urinates the same amount of urine is higher, which may indicate that the patient has symptoms of overactive bladder and needs corresponding treatment.

[0124] In summary, the present invention selects the initial urination point on the urine flow curve according to the change trend of the urine flow data points in the neighborhood range of each urine flow data point on the urine flow curve; adjusts the urine flow data according to the urine flow data in the neighborhood range at the time corresponding to the initial urination point to obtain the corrected urine flow data at each moment; obtains the normal urine volume influence probability of each bladder pressure data point according to the urine flow data and bladder pressure data at each moment, the maximum bladder pressure data affected by urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data; obtains the effective bladder pressure data, adjusts the bladder pressure data of the effective bladder pressure data point, and obtains the corrected bladder pressure data of each effective bladder pressure data point; and then obtains the urine volume-bladder pressure curve. The present invention obtains a more accurate urine volume-bladder pressure curve by correcting the urine volume data and the bladder pressure data, thereby improving the effect of bladder status diagnosis.

[0125] The present invention also provides a urinary catheter, which includes a urine volume-bladder pressure curve monitoring device, such as Figure 3 As shown, the urine volume-bladder pressure curve monitoring device includes a data acquisition module 301, a urine flow correction module 302, a bladder pressure correction module 303, and a urine volume-bladder pressure curve monitoring module 304:

[0126] Data acquisition module 301: acquiring the urine flow curve and bladder pressure curve of the patient;

[0127] Urine flow correction module 302: based on the change trend of urine flow data points in the neighborhood of each urine flow data point on the urine flow curve, the initial urination point on the urine flow curve is screened out; based on the urine flow data in the neighborhood at the time corresponding to the initial urination point, the urine flow data is adjusted to obtain the corrected urine flow data at each time;

[0128] Bladder pressure correction module 303: based on the bladder pressure data of each target bladder pressure data point and the degree of change and fluctuation of the bladder pressure data points within the corresponding neighborhood, the maximum bladder pressure data affected by the urine volume is screened out, and the target bladder pressure data point is the bladder pressure data point at a time greater than or equal to the time corresponding to the initial urination point on the bladder pressure curve; based on the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by the urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained; the bladder pressure data of the effective bladder pressure data point is adjusted according to the normal urine volume influence probability, and the corrected bladder pressure data of each effective bladder pressure data point is obtained; the effective bladder pressure data point is the bladder pressure data point at which the normal urine volume influence probability is greater than the set effective threshold;

[0129] Urine volume-bladder pressure curve monitoring module 304: obtains a urine volume-bladder pressure curve according to the corrected urine flow data and corrected bladder pressure data at the same time.

[0130] It should be understood that the urine volume-bladder pressure curve monitoring device provided in this embodiment is used to execute the above-mentioned bladder pressure information monitoring method, and therefore has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0131] The present invention also provides a storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the above-mentioned method for monitoring bladder pressure information are implemented.

[0132] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring bladder pressure information, characterized in that: The method comprises: Obtain the patient's urine flow curve and bladder pressure curve; According to the change trend of urine flow data points in the neighborhood of each urine flow data point on the urine flow curve, the initial urination point on the urine flow curve is screened out; according to the urine flow data in the neighborhood at the time corresponding to the initial urination point, the urine flow data is adjusted to obtain the corrected urine flow data at each time; According to the bladder pressure data of each target bladder pressure data point and the degree of change fluctuation of the bladder pressure data points in the corresponding neighborhood, the maximum bladder pressure data affected by the urine volume is screened out, the target bladder pressure data point being the bladder pressure data point at a time greater than or equal to the time corresponding to the initial urination point on the bladder pressure curve; according to the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by the urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained; The bladder pressure data of the effective bladder pressure data point is adjusted according to the normal urine volume influence probability to obtain the corrected bladder pressure data of each effective bladder pressure data point; the effective bladder pressure data point is a bladder pressure data point whose normal urine volume influence probability is greater than a set effective threshold value; A urine volume-bladder pressure curve is obtained based on the corrected urine flow data and the corrected bladder pressure data at the same time.

2. A method for monitoring bladder pressure information according to claim 1, characterized in that: The method for obtaining the initial urination point comprises: Obtaining the initial urination point probability of each urine flow data point according to the change trend of the urine flow data point within the neighborhood of each urine flow data point on the urine flow curve; The urine flow data point with the largest initial urination point probability among all urine flow data points is selected as the initial urination point.

3. A method for monitoring bladder pressure information according to claim 2, characterized in that: The method for obtaining the initial urination point probability includes: Calculate the difference between the urine flow data of each urine flow data point and the urine flow data of other urine flow data points within the corresponding neighborhood as the first difference; The sum of the first differences between each urine flow data point and all other subsequent urine flow data points is calculated as the first change feature; the sum of the first differences between each urine flow data point and all other previous urine flow data points is calculated as the second change feature; according to the difference between the first change feature and the second change feature, the initial urination point probability of each urine flow data point is obtained.

4. A method for monitoring bladder pressure information according to claim 1, characterized in that: The method for obtaining the corrected urine flow data comprises: Calculate the mean of all urine flow data within the neighborhood at the time corresponding to the initial urination point to obtain the urine flow data error; The difference between the urine flow data at each moment and the urine flow data error is calculated to obtain the corrected urine flow data at each moment.

5. The method for monitoring bladder pressure information according to claim 1, characterized in that: The method for obtaining the maximum bladder pressure data affected by urine volume comprises: For any target bladder pressure data point, the absolute value of the slope between the target bladder pressure data point and each other bladder pressure data point within the corresponding neighborhood is calculated, and the fluctuation degree of the absolute value of the slope is analyzed as the fluctuation degree of the change; According to the bladder pressure data of each target bladder pressure data point and the corresponding variation fluctuation degree, the influence degree of urine volume on each target bladder pressure data point is obtained. The bladder pressure data is positively correlated with the influence degree of urine volume, and the variation fluctuation degree is negatively correlated with the influence degree of urine volume. The bladder pressure data of the target bladder pressure data point that is most affected by the urine volume is used as the maximum bladder pressure data affected by the urine volume.

6. A method for monitoring bladder pressure information according to claim 1, characterized in that: The method for obtaining the normal urine volume influence probability includes: The bladder pressure data at each moment is normalized using the maximum bladder pressure data and the minimum bladder pressure data affected by the urine volume to obtain a first normalized value at each moment; The urine flow data at each moment is normalized using the maximum urine flow data and the minimum urine flow data to obtain a second normalized value at each moment; According to the difference between the first normalized value and the second normalized value at each moment, the normal urine volume influence probability of each bladder pressure data point is obtained, and the difference between the first normalized value and the second normalized value is negatively correlated with the normal urine volume influence probability.

7. A method for monitoring bladder pressure information according to claim 1, characterized in that: The method for obtaining the corrected bladder pressure data comprises: Determining whether it is necessary to adjust the bladder pressure data of each bladder pressure data point according to the normal urine volume influence probability of all bladder pressure data points; If adjustment is required, for any valid bladder pressure data point, the next valid bladder pressure data point of the valid bladder pressure data point is taken as a related point, and the cumulative value of the probability of normal urine volume between the valid bladder pressure data point and its related point is calculated; The ratio of the normal urine volume probability of the effective bladder pressure data point to the accumulated value is used as the weight of the effective bladder pressure data point; the ratio of the normal urine volume probability of the relevant point to the accumulated value is used as the weight of the relevant point; Corrected bladder pressure data of the effective bladder pressure data point is obtained by combining the effective bladder pressure data point with the weights of the relevant points and the corresponding bladder pressure data.

8. A method for monitoring bladder pressure information according to claim 7, characterized in that: The determining whether the bladder pressure data of each bladder pressure data point needs to be adjusted according to the normal urine volume influence probability of all bladder pressure data points comprises: The probability of normal urine volume of all bladder pressure data points is averaged. If the average is greater than the preset relevant threshold, adjustment is required, otherwise no adjustment is required.

9. A urinary catheter, characterized in that: The urinary catheter includes a urine volume-bladder pressure curve monitoring device, which includes a data acquisition module, a urine flow correction module, a bladder pressure correction module, and a urine volume-bladder pressure curve monitoring module: Data acquisition module: obtain the patient's urine flow curve and bladder pressure curve; Urine flow correction module: screening out the initial urination point on the urine flow curve according to the change trend of the urine flow data points within the neighborhood of each urine flow data point on the urine flow curve; The urine flow data is adjusted according to the urine flow data in the neighborhood range at the time corresponding to the initial urination point to obtain the corrected urine flow data at each time; Bladder pressure correction module: based on the bladder pressure data of each target bladder pressure data point and the degree of change and fluctuation of the bladder pressure data points within the corresponding neighborhood, the maximum bladder pressure data affected by the urine volume is screened out, the target bladder pressure data point is the bladder pressure data point on the bladder pressure curve at a time greater than or equal to the time corresponding to the initial urination point; based on the urine flow data and bladder pressure data at each time, the maximum bladder pressure data affected by the urine volume, the minimum bladder pressure data, the maximum urine flow data and the minimum urine flow data, the normal urine volume influence probability of each bladder pressure data point is obtained; The bladder pressure data of the effective bladder pressure data point is adjusted according to the normal urine volume influence probability to obtain the corrected bladder pressure data of each effective bladder pressure data point; the effective bladder pressure data point is a bladder pressure data point whose normal urine volume influence probability is greater than a set effective threshold value; Urine volume-bladder pressure curve monitoring module: obtains the urine volume-bladder pressure curve according to the corrected urine flow data and corrected bladder pressure data at the same time.

10. A storage medium, characterized in that: The storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of a bladder pressure information monitoring method as claimed in any one of claims 1 to 8 are implemented.

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