Intelligent urine flow monitoring and feedback system and method

Abstract

The invention relates to the technical field of urine flow monitoring, in particular to an intelligent urine flow monitoring and feedback system and method.The method comprises the steps that the real-time urine volume of a patient is monitored, and a urine volume time sequence is obtained; performing data processing on the urine volume time sequence to obtain a primary time sequence and a plurality of offspring time sequences; calculating a first analysis parameter, a second analysis parameter and a metabolism analysis parameter of the urine volume time sequence; and constructing an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolism analysis parameter, carrying out multiple times of training iteration on the analysis feedback model until the analysis feedback model converges, determining an analysis feedback method, and carrying out monitoring feedback on the real-time urine volume of the patient based on the analysis feedback method. According to the invention, the problem of the vacuum period from the occurrence of the health problem to the discovery of the health problem of the patient is solved, so that the health problem of the patient can be timely and effectively checked.

Description

Technical Field

[0001] The present invention relates to the technical field of urine flow monitoring, and in particular to an intelligent urine flow monitoring and feedback system and method. Background Art

[0002] Urine flow monitoring is a clinical examination method that evaluates a patient's urination function or fluid balance by measuring and recording parameters such as urine output, flow rate, and time. It is also an important tool for evaluating renal function, fluid balance, and urination function. It plays an irreplaceable role, especially in the management of critically ill patients and after surgery. By real-time monitoring of urine volume and urine flow pattern, medical staff can detect early abnormalities in patients, optimize treatment plans based on abnormalities, improve patient prognosis, and avoid worsening of the disease.

[0003] In the prior art, for example, when a patient is in the hospital, a catheter is usually connected to the patient, and a urine bag is connected to one end of the catheter. When nurses or doctors regularly change the patient's dressings and are on duty, the urine bag and the catheter are checked. There is a lack of real-time monitoring of urine flow, and there is a vacuum period from the time a patient's health problem occurs to the time it is discovered, and the patient's health problems cannot be checked in a timely and effective manner. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose an intelligent urine flow monitoring and feedback system and method.

[0005] The technical solution of the present invention is: an intelligent urine flow monitoring and feedback method, comprising S1, monitoring the real-time urine volume of the patient and obtaining the urine volume time series; S2, processing the urine volume time series to obtain an initial generation time series and several child generation time series; S3, calculating the first analysis parameter of the urine volume time series based on the primary time series and the plurality of child time series; S4, obtaining urine-related multidimensional information of historically healthy patients, and calculating a second analysis parameter of the urine volume time series based on the urine-related multidimensional information; S5, obtaining intervention information of the patient, and calculating metabolic analysis parameters of the urine volume time series based on the intervention information; S6. Construct an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, and perform multiple training iterations on the analysis feedback model until the analysis feedback model converges, determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method.

[0006] Preferably, for S2, the method for processing the urine volume time series data includes: Set the sampling period, calibrate the sampling start time and the sampling end time based on the duration of the sampling period, and cut the urine volume time series based on the duration of the sampling period to obtain the corresponding primary time series; The real-time urine flow F(t) of the primary time series is calculated by the following formula: ; Where, F(t) is the real-time urine flow; is the time-varying value; is the urine volume at the current time; The urine volume after the time change value at the current time; Set the urination record threshold. When the real-time urine flow rate is greater than the urination record threshold, add a start mark to the urine volume time series. When the real-time urine flow rate is no greater than the urination record threshold, add an end mark to the urine volume time series, and record the number of times the urination record threshold is triggered as the number of urinations. Construct several child time series of the initial time series based on the start tag and the end tag.

[0007] Preferably, for S3, the method for calculating the first analysis parameter of the urine volume time series is: Get the total urine volume represented by the initial time series and mark it as ; The first analysis parameter of the patient is calculated by the following formula: ; Where A1 is the first analysis parameter; The starting time of the offspring time series, is the end time of the offspring time series; i is the number of the offspring time series, i is a positive integer, i∈[1,n], n is the total number of offspring time series; the start mark and end mark of the offspring time series correspond one to one.

[0008] Preferably, with respect to S4, the method for calculating the second analysis parameter of the urine volume time series based on urine-related multidimensional information includes: Obtain the age, urination time, urination frequency and daily urine volume of historical healthy patients, generate urine-related multidimensional information, and establish a urine-related multidimensional information database; The patient's age is taken as the target age, the floating age range of the target age is obtained, and all ages included in the floating age range are input as keywords into the urine-related multidimensional information database for retrieval, the retrieval results of different ages are merged to obtain the target data, the target data is subjected to the first data processing to obtain the weighted average daily urine volume and the weighted average urination frequency, and the weighted average daily urine volume and the weighted average urination frequency are taken as the first standard value and the second standard value, respectively.

[0009] Preferably, with respect to S4, the method for calculating the second analysis parameter of the urine volume time series based on the urine-related multidimensional information further includes: Taking the daytime period as screening information, screening the urine-related multidimensional information in the search data based on the screening information, marking the screened urine-related multidimensional information as first information, and marking the remaining urine-related multidimensional information as second information; Counting the number of the first information and the second information respectively to obtain the total number of the first information and the total number of the second information, and using the total number of the first information and the total number of the second information as the first correction parameter and the second correction parameter respectively; Obtain the coverage period of the initial time series and mark it as the representation period; The second analysis parameter of the patient is calculated by the following formula: ; Where A2 is the second analysis parameter; and are the first standard value and the second standard value respectively; and is the weight coefficient; Ts is the span length of the characterization period; is a correction function based on the characterization period.

[0010] Preferably, for S4, the expression of the correction function is as follows: ; In the formula, is the first correction parameter, is the second correction parameter.

[0011] Preferably, for S5, the method for calculating the metabolic analysis parameters of the urine volume time series based on the intervention information is: Obtain the type of fluid ingested by the patient in the intervention period before each characteristic period and the corresponding fluid input amount; Get the characteristic urine volume corresponding to the offspring time series; It should be noted that the characteristic urine volume can be based on the formula in S3 The integral part is obtained; Metabolic analysis parameters were calculated by the following formula; ; In the formula, D is the metabolic analysis parameter; Characteristic urine volume; is the amount of liquid input; is the fluid metabolism coefficient; j is the fluid type number, j is a positive integer; γ is the basal metabolism coefficient, which is obtained based on big data analysis of patients in the floating age group; Tg is the duration of the intervention period.

[0012] Preferably, for S6, the expression of the analysis feedback model is: ; In the formula, To analyze the feedback value, , and They are the weight coefficients of the first analysis parameter A1, the second analysis parameter A2 and the metabolic analysis parameter D respectively.

[0013] Preferably, for S6, determining the analysis feedback method, and monitoring and providing feedback on the patient's real-time urine volume based on the analysis feedback method includes: The analysis feedback model is trained and iterated to make the analysis feedback value satisfy ≤Ay, Ay is the minimum risk threshold; The analysis feedback method is: Calculate the first analysis parameter, the second analysis parameter and the metabolic analysis parameter of the current patient, and input them into the training converged analysis feedback model to obtain the analysis feedback value, and determine whether the analysis feedback value is greater than the minimum risk threshold; If the analysis feedback value is less than the minimum risk threshold, normal feedback is given and the patient's urine flow is continuously monitored and analyzed; If the analysis feedback value is greater than the minimum risk threshold, an abnormal feedback is given to notify the nurses and doctors to conduct physiological examinations on the patient in a timely manner.

[0014] The present invention also discloses an intelligent urine flow monitoring and feedback system, which applies the above intelligent urine flow monitoring and feedback method, and specifically includes: The data real-time monitoring module is used to monitor the patient's real-time urine volume and obtain the urine volume time series; A data preprocessing module is used to process the urine volume time series to obtain an initial generation time series and several child generation time series; A first data analysis module, used for calculating a first analysis parameter of a urine volume time series based on a primary time series and a plurality of child time series; A second data analysis module is used to obtain urine-related multidimensional information of historically healthy patients and calculate a second analysis parameter of the urine volume time series based on the urine-related multidimensional information; A third data analysis module is used to obtain the patient's intervention information and calculate the metabolic analysis parameters of the urine volume time series based on the intervention information; The model training and feedback module is used to construct an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, and to perform multiple training iterations on the analysis feedback model until the analysis feedback model converges, determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method.

[0015] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: The first analysis parameter is obtained by real-time monitoring and analysis of the patient's urine volume, the second analysis parameter is calculated by combining the patient's urine-related multi-dimensional information, and the metabolic analysis parameter is calculated by combining the patient's intervention information. The first analysis parameter, the second analysis parameter and the metabolic analysis parameter reflect the patient's urination pressure ability, the comprehensive assessment ability of urine volume and urination frequency, and the liquid metabolism ability. The three parameter analysis feedback models are iterated and trained to determine the analysis feedback method, and the patient's real-time urine volume is monitored and fed back based on the analysis feedback method, which solves the vacuum period between the occurrence of health problems and the discovery of the patients, and enables the timely and effective screening of the patients' health problems. The present invention also has the advantage of simple data acquisition. By monitoring the real-time information of the patient's urine volume, combining the patient's urine-related multi-dimensional information and intervention information, and analyzing the feedback model, it is possible to provide timely and correct feedback on the patient's urine volume information, and the monitoring cost from monitoring to feedback is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the method of embodiment 1 proposed by the present invention. DETAILED DESCRIPTION

[0017] Embodiment 1, as Figure 1 As shown, the intelligent urine flow monitoring and feedback method proposed by the present invention comprises the following steps: S1. Monitor the patient's real-time urine volume and obtain the urine volume time series; S2, processing the urine volume time series to obtain an initial generation time series and several child generation time series; For S2, the method for processing the urine volume time series data includes: Set the sampling period, calibrate the sampling start time and the sampling end time based on the duration of the sampling period, and cut the urine volume time series based on the duration of the sampling period to obtain the corresponding primary time series; The real-time urine flow F(t) of the primary time series is calculated by the following formula: ; Where, F(t) is the real-time urine flow; is the time-varying value; is the urine volume at the current time; The urine volume after the time change value at the current time; Set the urination record threshold. When the real-time urine flow rate is greater than the urination record threshold, add a start mark to the urine volume time series. When the real-time urine flow rate is no greater than the urination record threshold, add an end mark to the urine volume time series, and record the number of times the urination record threshold is triggered as the number of urinations. Construct several sub-generation time series of the initial generation time series based on the start mark and the end mark; S3, calculating the first analysis parameter of the urine volume time series based on the primary time series and the plurality of child time series; For S3, the method for calculating the first analysis parameter of the urine volume time series is: Get the total urine volume represented by the initial time series and mark it as ; The first analysis parameter of the patient is calculated by the following formula: ; Where A1 is the first analysis parameter; The starting time of the offspring time series, is the end time of the child time series; i is the number of the child time series, i is a positive integer, i∈[1,n], n is the total number of child time series; the start mark and end mark of the child time series correspond one to one; It should be noted that the first analysis parameter A1 can be understood as the patient's urination pressure ability. The higher the first analysis parameter A1 is, the less urine volume is not judged as urination, which further reflects that the urine volume produced by the urine flow rate not meeting the standard due to insufficient urination pressure is less. S4, obtaining urine-related multidimensional information of historically healthy patients, and calculating a second analysis parameter of the urine volume time series based on the urine-related multidimensional information; With respect to S4, the method for calculating the second analysis parameter of the urine volume time series based on the urine-related multidimensional information includes: Obtain the age, urination time, urination frequency and daily urine volume of historical healthy patients, generate urine-related multidimensional information, and establish a urine-related multidimensional information database; The patient's age is taken as the target age, the floating age range of the target age is obtained, and all ages included in the floating age range are input as keywords into the urine-related multidimensional information database for retrieval, the retrieval results of different ages are merged to obtain the target data, the target data is subjected to the first data processing to obtain the weighted average daily urine volume and the weighted average urination frequency, and the weighted average daily urine volume and the weighted average urination frequency are taken as the first standard value and the second standard value, respectively.

[0018] Specifically, the floating age group is expressed by the relation Calculated; where FL is the floating age group, ML is the target age, and Fk is the age floating span; The daytime period is used as screening information, and the urine-related multidimensional information in the search data is screened based on the screening information, and the plurality of urine-related multidimensional information obtained by screening are all marked as the first information, and the remaining plurality of urine-related multidimensional information are all marked as the second information; it should be noted that the daytime period is used to divide the whole day time period into the daytime period and the nighttime period, and the daytime period shall be based on the existing period planning; Counting the number of the first information and the second information respectively to obtain the total number of the first information and the total number of the second information, and using the total number of the first information and the total number of the second information as the first correction parameter and the second correction parameter respectively; Obtain the coverage period of the initial time series and mark it as the representation period; The second analysis parameter of the patient is calculated by the following formula: ; Where A2 is the second analysis parameter; and are the first standard value and the second standard value respectively; and is the weight coefficient; Ts is the span length of the characterization period; is a correction function based on the characterization period; It should be noted that the second analysis parameter can be understood as the comprehensive assessment ability of the patient's urine volume and urination frequency during the characterization period. The higher the comprehensive assessment ability, the higher the fit between the patient's urination ability and the first standard value and the second standard value. S5, obtaining intervention information of the patient, and calculating metabolic analysis parameters of the urine volume time series based on the intervention information; For S5, the method for calculating the metabolic analysis parameters of the urine volume time series based on the intervention information is: Obtain the type of fluid ingested by the patient in the intervention period before each characteristic period and the corresponding fluid input amount; Get the characteristic urine volume corresponding to the offspring time series; It should be noted that the characteristic urine volume can be based on the formula in S3 The integral part is obtained; Metabolic analysis parameters were calculated by the following formula; ; In the formula, D is the metabolic analysis parameter; Characteristic urine volume; is the amount of liquid input; is the fluid metabolism coefficient; j is the fluid type number, j is a positive integer; γ is the basic metabolism coefficient, which is obtained based on big data analysis of patients in the floating age group; Tg is the duration of the intervention period; It should be noted that the metabolic analysis parameters can be understood as the patient's ability to metabolize fluids. The higher the metabolic analysis parameters, the higher the patient's metabolic capacity and the higher the urine output; S6. constructing an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, and performing multiple training iterations on the analysis feedback model until the analysis feedback model converges, determining an analysis feedback method, and monitoring and providing feedback on the patient's real-time urine volume based on the analysis feedback method; For S6, the expression of the analytical feedback model is: ; In the formula, To analyze the feedback value, , and are the weight coefficients of the first analysis parameter A1, the second analysis parameter A2 and the metabolic analysis parameter D respectively; Determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method, including: The analysis feedback model is trained and iterated to make the analysis feedback value satisfy ≤Ay, Ay is the minimum risk threshold; The analysis feedback method is: Calculate the first analysis parameter, the second analysis parameter and the metabolic analysis parameter of the current patient, and input them into the training converged analysis feedback model to obtain the analysis feedback value, and determine whether the analysis feedback value is greater than the minimum risk threshold; If the analysis feedback value is less than the minimum risk threshold, normal feedback is given and the patient's urine flow is continuously monitored and analyzed; If the analysis feedback value is greater than the minimum risk threshold, an abnormal feedback is given to notify the nurses and doctors to conduct physiological examinations on the patient in a timely manner.

[0019] Example 2, based on the floating age group and fluid metabolism coefficient of S5 in Example 1, explains: For example, the target age is 25, the age floating threshold is 3, the floating age range of the sample is [22, 28], the intervention period is half an hour, and the intervention coefficient is explained based on 50 samples constructed based on the following five sample sets: Sample set 1: 10 22-year-old adult patients produced about 300 ml of urine after drinking only 500 ml of water half an hour later; Sample set 2: 5 23-year-old adult patients produced about 250 ml of urine half an hour after drinking only 500 ml of water; Sample set 3: 15 24-year-old adult patients produced about 400 ml of urine after drinking only 500 ml of water half an hour later; Sample set 4: 20 25-year-old adult patients produced about 450 ml of urine half an hour after drinking only 500 ml of water; Sample set 5: 5 26-year-old adult patients produced about 500 ml of urine half an hour after drinking only 500 ml of water; Then the metabolic coefficient of water is (10×300+5×250+15×400+20×450+5×500) ÷ 50×500 = 0.87.

[0020] Embodiment 3, an intelligent urine flow monitoring and feedback system proposed by the present invention is applied to an intelligent urine flow monitoring and feedback method proposed in Embodiment 1, and its specific implementation steps are as follows: The data real-time monitoring module is used to monitor the patient's real-time urine volume and obtain the urine volume time series; A data preprocessing module is used to process the urine volume time series to obtain an initial generation time series and several child generation time series; A first data analysis module, used for calculating a first analysis parameter of a urine volume time series based on a primary time series and a plurality of child time series; A second data analysis module is used to obtain urine-related multidimensional information of historically healthy patients and calculate a second analysis parameter of the urine volume time series based on the urine-related multidimensional information; A third data analysis module is used to obtain the patient's intervention information and calculate the metabolic analysis parameters of the urine volume time series based on the intervention information; The model training and feedback module is used to construct an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, and to perform multiple training iterations on the analysis feedback model until the analysis feedback model converges, determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method.

[0021] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. An intelligent urine flow monitoring and feedback method, characterized in that: The following steps are involved: S1. Monitor the patient's real-time urine volume and obtain the urine volume time series; S2, processing the urine volume time series to obtain an initial generation time series and several child generation time series; S3, calculating the first analysis parameter of the urine volume time series based on the primary time series and the plurality of child time series; S4, obtaining urine-related multidimensional information of historically healthy patients, and calculating a second analysis parameter of the urine volume time series based on the urine-related multidimensional information; S5, obtaining intervention information of the patient, and calculating metabolic analysis parameters of the urine volume time series based on the intervention information; S6. Construct an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, and perform multiple training iterations on the analysis feedback model until the analysis feedback model converges, determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method.

2. An intelligent urine flow monitoring and feedback method according to claim 1, characterized in that: For S2, the method for processing the urine volume time series data includes: Set the sampling period, calibrate the sampling start time and the sampling end time based on the duration of the sampling period, and cut the urine volume time series based on the duration of the sampling period to obtain the corresponding primary time series; The real-time urine flow F(t) of the primary time series is calculated by the following formula: ； Where, F(t) is the real-time urine flow; is the time-varying value; is the urine volume at the current time; The urine volume after the time change value at the current time; Set the urination record threshold. When the real-time urine flow rate is greater than the urination record threshold, add a start mark to the urine volume time series. When the real-time urine flow rate is no greater than the urination record threshold, add an end mark to the urine volume time series, and record the number of times the urination record threshold is triggered as the number of urinations. Construct several child time series of the initial time series based on the start tag and the end tag.

3. An intelligent urine flow monitoring and feedback method according to claim 2, characterized in that: For S3, the method for calculating the first analysis parameter of the urine volume time series is: Get the total urine volume represented by the initial time series and mark it as ; The first analysis parameter of the patient is calculated by the following formula: ； Where A1 is the first analysis parameter; The starting time of the offspring time series, is the end time of the child time series; i is the number of the child time series, i is a positive integer, i∈[1,n], n is the total number of child time series; the start mark and end mark of the child time series correspond one to one.

4. The intelligent urine flow monitoring and feedback method according to claim 3, characterized in that: With respect to S4, the method for calculating the second analysis parameter of the urine volume time series based on the urine-related multidimensional information includes: Obtain the age, urination time, urination frequency and daily urine volume of historical healthy patients, generate urine-related multidimensional information, and establish a urine-related multidimensional information database; The patient's age is taken as the target age, the floating age range of the target age is obtained, and all ages included in the floating age range are input as keywords into the urine-related multidimensional information database for retrieval, the retrieval results of different ages are merged to obtain the target data, the target data is subjected to the first data processing to obtain the weighted average daily urine volume and the weighted average urination frequency, and the weighted average daily urine volume and the weighted average urination frequency are taken as the first standard value and the second standard value, respectively.

5. An intelligent urine flow monitoring and feedback method according to claim 4, characterized in that: With respect to S4, the method for calculating the second analysis parameter of the urine volume time series based on the urine-related multi-dimensional information further includes: Taking the daytime period as screening information, screening the urine-related multidimensional information in the search data based on the screening information, marking the screened urine-related multidimensional information as first information, and marking the remaining urine-related multidimensional information as second information; Counting the number of the first information and the second information respectively to obtain the total number of the first information and the total number of the second information, and using the total number of the first information and the total number of the second information as the first correction parameter and the second correction parameter respectively; Obtain the coverage period of the initial time series and mark it as the representation period; The second analysis parameter of the patient is calculated by the following formula: ； Where A2 is the second analysis parameter; and are the first standard value and the second standard value respectively; and is the weight coefficient; Ts is the span length of the characterization period; is a correction function based on the characterization period.

6. The intelligent urine flow monitoring and feedback method according to claim 5, characterized in that: For S4, the expression of the correction function is as follows: ； In the formula, is the first correction parameter, is the second correction parameter.

7. An intelligent urine flow monitoring and feedback method according to claim 6, characterized in that: For S5, the method for calculating the metabolic analysis parameters of the urine volume time series based on the intervention information is: Obtain the type of fluid ingested by the patient in the intervention period before each characteristic period and the corresponding fluid input amount; Get the characteristic urine volume corresponding to the offspring time series; It should be noted that the characteristic urine volume can be based on the formula in S3 The integral part is obtained; Metabolic analysis parameters were calculated by the following formula; ； In the formula, D is the metabolic analysis parameter; Characteristic urine volume; is the amount of liquid input; is the fluid metabolism coefficient; j is the fluid type number, j is a positive integer; γ is the basal metabolism coefficient, which is obtained based on big data analysis of patients in the floating age group; Tg is the duration of the intervention period.

8. An intelligent urine flow monitoring and feedback method according to claim 7, characterized in that: For S6, the expression of the analytical feedback model is: ； In the formula, To analyze the feedback value, , and They are the weight coefficients of the first analysis parameter A1, the second analysis parameter A2 and the metabolic analysis parameter D respectively.

9. An intelligent urine flow monitoring and feedback method according to claim 8, characterized in that: For S6, determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method, including: The analysis feedback model is trained and iterated to make the analysis feedback value satisfy ≤Ay, Ay is the minimum risk threshold; The analysis feedback method is: Calculate the first analysis parameter, the second analysis parameter and the metabolic analysis parameter of the current patient, and input them into the training converged analysis feedback model to obtain the analysis feedback value, and determine whether the analysis feedback value is greater than the minimum risk threshold; If the analysis feedback value is less than the minimum risk threshold, normal feedback is given and the patient's urine flow is continuously monitored and analyzed; If the analysis feedback value is greater than the minimum risk threshold, an abnormal feedback is given to notify the nurses and doctors to conduct physiological examinations on the patient in a timely manner.

10. An intelligent urine flow monitoring and feedback system, using an intelligent urine flow monitoring and feedback method as claimed in claim 9, characterized in that: include: The data real-time monitoring module is used to monitor the patient's real-time urine volume and obtain the urine volume time series; A data preprocessing module is used to process the urine volume time series to obtain an initial generation time series and several child generation time series; A first data analysis module, used for calculating a first analysis parameter of a urine volume time series based on a primary time series and a plurality of child time series; A second data analysis module is used to obtain urine-related multidimensional information of historically healthy patients and calculate a second analysis parameter of the urine volume time series based on the urine-related multidimensional information; A third data analysis module is used to obtain the patient's intervention information and calculate the metabolic analysis parameters of the urine volume time series based on the intervention information; The model training and feedback module is used to construct an analysis feedback model based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, and to perform multiple training iterations on the analysis feedback model until the analysis feedback model converges, determine the analysis feedback method, and monitor and feedback the patient's real-time urine volume based on the analysis feedback method.

Images