An intelligent urine flow monitoring and feedback system and method

Abstract

The present invention relates to the technical field of urine flow monitoring, and specifically to an intelligent urine flow monitoring and feedback system and method, which includes monitoring the real-time urine volume of a patient to obtain a urine volume time series; performing data processing on the urine volume time series to obtain a primary time series and several offspring time series; calculating a first analysis parameter, a second analysis parameter, and a metabolic analysis parameter of the urine volume time series; 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 feedbacking the real-time urine volume of the patient based on the analysis feedback method. The present invention solves the problem of the vacuum period existing from the occurrence of a health problem in a patient to its discovery, enabling timely and effective investigation of the patient's health problems.

Description

Technical Field

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

[0002] Urine flow monitoring is a clinical examination method for evaluating a patient's urination function or body fluid balance state by measuring and recording parameters such as urine output, flow rate, and time. It is also an important tool for evaluating renal function, body fluid balance, and urination function. Especially in critically ill patients and postoperative management, it plays an irreplaceable role. By real-time monitoring urine volume and urine flow patterns, medical staff can detect early abnormalities in patients, optimize treatment plans for the abnormalities, and improve the prognosis of patients, avoiding the deterioration of the condition.

[0003] In the prior art, for example, during a patient's hospitalization, the patient is usually connected to a urinary catheter, and a urine bag is connected to one end of the urinary catheter. During the regular dressing change and on-duty process of nurses or doctors for the patient, the urine bag and urinary catheter are checked. There is a lack of real-time monitoring of urine flow, and there is a vacuum period from the occurrence of a patient's health problem to its discovery, and the patient's health problems cannot be effectively investigated in a timely manner. Summary of the Invention

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

[0005] The technical solution of the present invention: An intelligent urine flow monitoring and feedback method includes S1, monitoring the real-time urine volume of a patient to obtain a urine volume time series;

[0006] S2, performing data processing on the urine volume time series to obtain a primary time series and several sub-time series;

[0007] S3, calculating a first analysis parameter of the urine volume time series based on the primary time series and several sub-time series;

[0008] S4, obtaining urine-related multi-dimensional information of historical healthy patients, and calculating a second analysis parameter of the urine volume time series based on the urine-related multi-dimensional information;

[0009] S5, obtaining intervention information of the patient, and calculating a metabolic analysis parameter of the urine volume time series based on the intervention information;

[0010] S6, constructing an analysis and 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 and feedback model until the analysis and feedback model converges, determining the analysis and feedback method, and monitoring and feedback the real-time urine volume of the patient based on the analysis and feedback method.

[0011] Preferably, for S2, the method for processing the urine volume time series includes:

[0012] Set a sampling period, calibrate the sampling start time and sampling end time based on the duration of the sampling period, and cut the urine volume time series based on the sampling period duration to obtain the corresponding initial time series;

[0013] Calculate the real-time urine flow rate F(t) of the initial time series through the following formula:

[0014] ;

[0015] In the formula, F(t) is the real-time urine flow rate; is the time change value; is the urine volume at the current time; is the urine volume after the current time passes through the time change value;

[0016] Set a 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 until the real-time urine flow rate is not 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;

[0017] Construct a number of sub-time series of the initial time series based on the start mark and end mark.

[0018] Preferably, for S3, the method for calculating the first analysis parameter of the urine volume time series is:

[0019] Obtain the total urine volume represented by the initial time series and mark it as ;

[0020] Calculate the first analysis parameter of the patient through the following formula:

[0021] ;

[0022] In the formula, A1 is the first analysis parameter; The start time of the sub-time series, is the end time of the sub-time series; i is the number of the sub-time series, i is a positive integer, i ∈ [1, n], n is the total number of sub-time series; the start mark and end mark of the sub-time series correspond one by one.

[0023] Preferably, for S4, the method for calculating the second analysis parameter of the urine volume time series based on urine-related multi-dimensional information includes:

[0024] Obtain the age, urination time, number of urinations, and daily urine volume of historical healthy patients, generate urine-related multi-dimensional information, and establish a urine-related multi-dimensional information database;

[0025] Taking the age of the patient as the target age, obtaining the floating age range of the target age, and respectively taking all the ages included in the floating age range as keywords and inputting them into the urine-related multi-dimensional information database for retrieval, merging the retrieval results of different ages to obtain target data, performing first data processing on the target data to obtain the weighted average of daily urine volume and the weighted average of urination frequency, and respectively taking the weighted average of daily urine volume and the weighted average of urination frequency as the first standard value and the second standard value.

[0026] Preferably, for S4, the method for calculating the second analysis parameter of the urine volume time series based on urine-related multi-dimensional information further includes:

[0027] Taking the daytime period as the screening information, screening the urine-related multi-dimensional information in the retrieval data based on the screening information, marking several pieces of urine-related multi-dimensional information obtained by screening as the first information, and marking the remaining several pieces of urine-related multi-dimensional information as the second information;

[0028] Respectively counting the quantities of the first information and the second information to obtain the total number of the first information and the total number of the second information, and respectively taking 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;

[0029] Obtaining the coverage period of the initial time series and marking it as the characterization period;

[0030] Calculating the second analysis parameter of the patient through the following formula:

[0031] ;

[0032] In the formula, A2 is the second analysis parameter; and are the first standard value and the second standard value respectively; and are the weight coefficients; Ts is the span duration of the characterization period; is the correction function based on the characterization period.

[0033] Preferably, for S4, the expression of the correction function is as follows:

[0034] ;

[0035] In the formula, is the first correction parameter, is the second correction parameter.

[0036] Preferably, for S5, the method for calculating the metabolic analysis parameter of the urine volume time series based on the intervention information is:

[0037] Obtain the types of fluids ingested and the corresponding fluid input volumes during the previous intervention period for each characteristic period of the patient;

[0038] Obtain the characteristic urine volume corresponding to the offspring time series;

[0039] It should be noted that the characteristic urine volume can be obtained based on the integral part of the formula in S3; of;

[0040] Calculate the metabolic analysis parameters through the following formula;

[0041] ;

[0042] In the formula, D is the metabolic analysis parameter; is the characteristic urine volume; is the fluid input volume; is the fluid metabolism coefficient; j is the fluid type number, and j is a positive integer; γ is the basal metabolism coefficient, obtained through big data analysis of patients in the floating age range; Tg is the duration of the intervention period.

[0043] Preferably, for S6, the expression of the analysis feedback model is:

[0044] ;

[0045] In the formula, is the analysis 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.

[0046] Preferably, for S6, determine the analysis feedback method, and monitor and feedback the real-time urine volume of the patient based on the analysis feedback method, including:

[0047] Train and iterate the analysis feedback model so that the analysis feedback value satisfies ≤ Ay, where Ay is the minimum risk threshold;

[0048] The analysis feedback method is:

[0049] Calculate the first analysis parameter, the second analysis parameter, and the metabolic analysis parameter of the current patient, and input them into the trained and converged analysis feedback model to obtain the analysis feedback value, and judge whether the analysis feedback value is greater than the minimum risk threshold;

[0050] If the analysis feedback value is less than the minimum risk threshold, perform normal feedback, and continuously monitor and analyze the urine flow of the patient;

[0051] If the analysis feedback value is greater than the minimum risk threshold, abnormal feedback is performed to notify the nurse and doctor to conduct a physical examination on the patient in a timely manner.

[0052] The present invention also discloses an intelligent urine flow monitoring and feedback system, which applies the above-mentioned intelligent urine flow monitoring and feedback method, and specifically includes:

[0053] A data real-time monitoring module, which is used to monitor the real-time urine volume of the patient and obtain the urine volume time series;

[0054] A data preprocessing module, which is used to process the urine volume time series to obtain the primary time series and several offspring time series;

[0055] A first data analysis module, which is used to calculate the first analysis parameter of the urine volume time series based on the primary time series and several offspring time series;

[0056] A second data analysis module, which is used to obtain the multi-dimensional urine-related information of historical healthy patients and calculate the second analysis parameter of the urine volume time series based on the multi-dimensional urine-related information;

[0057] A third data analysis module, which is used to obtain the intervention information of the patient and calculate the metabolic analysis parameter of the urine volume time series based on the intervention information;

[0058] A model training and feedback module, which is used to 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 real-time urine volume of the patient based on the analysis feedback method.

[0059] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0060] By performing real-time monitoring and analysis of the patient's urine volume, the first analysis parameter is obtained. By combining the multi-dimensional urine-related information of the patient, the second analysis parameter is calculated. By combining the intervention information of the patient, the metabolic analysis parameter is calculated. Based on the first analysis parameter, the second analysis parameter and the metabolic analysis parameter, the comprehensive evaluation ability of the patient's urination pressure ability, urine volume and urination frequency, and the metabolic ability of the liquid are reflected. Based on the three parameters, the analysis feedback model is iterated and trained to determine the analysis feedback method, and the real-time urine volume of the patient is monitored and feedback based on the analysis feedback method, which solves the problem of the vacuum period existing from the occurrence of the patient's health problem to its discovery, and enables timely and effective investigation of the patient's health problem;

[0061] The present invention also has the advantages of simple data acquisition. By monitoring the real-time information of the patient's urine volume, combining the multi-dimensional information related to the patient's urine and the intervention information, and through the analysis and feedback model, it can timely and correctly feedback on the patient's urine volume information, and the monitoring cost from monitoring to feedback is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flowchart of the method of Embodiment 1 proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Embodiment 1, as Figure 1 shown, an intelligent urine flow monitoring and feedback method proposed by the present invention includes the following steps:

[0064] S1. Monitor the real-time urine volume of the patient to obtain the urine volume time series;

[0065] S2. Process the urine volume time series to obtain the primary time series and several sub-generation time series;

[0066] For S2, the method for processing the urine volume time series includes:

[0067] Set the sampling period, calibrate the sampling start time and sampling end time based on the duration of the sampling period, and cut the urine volume time series based on the sampling period duration to obtain the corresponding primary time series;

[0068] Calculate the real-time urine flow rate F(t) of the primary time series through the following formula:

[0069] ;

[0070] In the formula, F(t) is the real-time urine flow rate; is the time change value; is the urine volume at the current time; is the urine volume after the current time passes the time change value;

[0071] 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 until the real-time urine flow rate is not 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;

[0072] Construct several sub-generation time series of the primary time series based on the start mark and the end mark;

[0073] S3. Calculate the first analysis parameter of the urine volume time series based on the primary time series and several sub-generation time series;

[0074] For S3, the method for calculating the first analysis parameter of the urine volume time series is:

[0075] Obtain the total urine volume represented by the initial time series and label it as ;

[0076] Calculate the first analysis parameter of the patient through the following formula:

[0077] ;

[0078] In the formula, A1 is the first analysis parameter; The starting time of the offspring time series, is the ending 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 starting marker and the ending marker of the offspring time series correspond one by one;

[0079] 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, that is, the less urine volume generated that is not judged as urination, and further reflects that the less urine volume is generated due to the substandard urine flow rate caused by insufficient urination pressure;

[0080] S4. Obtain the urine-related multi-dimensional information of historical healthy patients, and calculate the second analysis parameter of the urine volume time series based on the urine-related multi-dimensional information;

[0081] Regarding S4, the method for calculating the second analysis parameter of the urine volume time series based on the urine-related multi-dimensional information includes:

[0082] Obtain the age, urination time, urination frequency and daily urine volume of historical healthy patients, generate urine-related multi-dimensional information, and establish a urine-related multi-dimensional information database;

[0083] Take the patient's age as the target age, obtain the floating age range of the target age, and input all the ages included in the floating age range as keywords into the urine-related multi-dimensional information database for retrieval. Merge the retrieval results of different ages to obtain the target data. Perform the first data processing on the target data to obtain the weighted average of the daily urine volume and the weighted average of the urination frequency, and use the weighted average of the daily urine volume and the weighted average of the urination frequency as the first standard value and the second standard value respectively.

[0084] Specifically, the floating age range is calculated through the relational formula ; in the formula, FL is the floating age range, ML is the target age, and Fk is the age floating span;

[0085] 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;

[0086] 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;

[0087] Obtain the coverage period of the initial time series and mark it as the representation period;

[0088] The second analysis parameter of the patient is calculated by the following formula:

[0089] ;

[0090] 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;

[0091] 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.

[0092] S5, obtaining intervention information of the patient, and calculating metabolic analysis parameters of the urine volume time series based on the intervention information;

[0093] For S5, the method for calculating the metabolic analysis parameters of the urine volume time series based on the intervention information is:

[0094] Obtain the type of fluid ingested by the patient in the intervention period before each characteristic period and the corresponding fluid input amount;

[0095] Get the characteristic urine volume corresponding to the offspring time series;

[0096] It should be noted that the characteristic urine volume can be based on the formula in S3 The integral part is obtained;

[0097] Metabolic analysis parameters were calculated by the following formula;

[0098] ;

[0099] In the formula, D is the metabolic analysis parameter; is the characteristic urine volume; is the liquid input volume; is the liquid metabolism coefficient; j is the liquid type number, and j is a positive integer; γ is the basal metabolism coefficient, obtained based on big data analysis of patients in the floating age group; Tg is the duration of the intervention period;

[0100] It should be noted that the metabolic analysis parameter can be understood as the patient's metabolic ability for liquids. The higher the metabolic analysis parameter, the higher the patient's metabolic ability and the higher the urine output.

[0101] 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;

[0102] Regarding S6, the expression of the analysis feedback model is:

[0103] ;

[0104] In the formula, is the analysis 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;

[0105] Determine the analysis feedback method, and monitoring and feedback the patient's real-time urine volume based on the analysis feedback method includes:

[0106] Train and iterate the analysis feedback model to make the analysis feedback value satisfy ≤ Ay, where Ay is the minimum risk threshold;

[0107] The analysis feedback method is:

[0108] Calculate the first analysis parameter, the second analysis parameter, and the metabolic analysis parameter of the current patient, and input them into the trained and converged analysis feedback model to obtain the analysis feedback value, and judge whether the analysis feedback value is greater than the minimum risk threshold;

[0109] If the analysis feedback value is less than the minimum risk threshold, perform normal feedback, continuously monitor and analyze the patient's urine flow;

[0110] If the analysis feedback value is greater than the minimum risk threshold, perform abnormal feedback and notify the nurse and doctor to conduct a physical examination on the patient in a timely manner.

[0111] Example 2. Based on the floating age range and liquid metabolism coefficient in S5 of Example 1, an explanation is given:

[0112] Exemplarily, 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 50 samples constructed based on the following five sample sets are used to explain the intervention coefficient:

[0113] Sample set one: The urine volume produced by 10 adult patients aged 22 after only drinking 500 ml of water for half an hour is about 300 ml;

[0114] Sample set two: The urine volume produced by 5 adult patients aged 23 after only drinking 500 ml of water for half an hour is about 250 ml;

[0115] Sample set three: The urine volume produced by 15 adult patients aged 24 after only drinking 500 ml of water for half an hour is about 400 ml;

[0116] Sample set four: The urine volume produced by 20 adult patients aged 25 after only drinking 500 ml of water for half an hour is about 450 ml;

[0117] Sample set five: The urine volume produced by 5 adult patients aged 26 after only drinking 500 ml of water for half an hour is about 500 ml;

[0118] Then the metabolism coefficient of water is (10×300 + 5×250 + 15×400 + 20×450 + 5×500)÷50×500 = 0.87.

[0119] Example 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 Example 1, and its specific implementation steps are as follows:

[0120] A data real-time monitoring module for monitoring the real-time urine volume of a patient and obtaining a urine volume time series;

[0121] A data preprocessing module for processing the urine volume time series to obtain a primary time series and several sub-generation time series;

[0122] A first data analysis module for calculating a first analysis parameter of the urine volume time series based on the primary time series and several sub-generation time series;

[0123] A second data analysis module for obtaining the urine-related multi-dimensional information of historical healthy patients and calculating a second analysis parameter of the urine volume time series based on the urine-related multi-dimensional information;

[0124] The third data analysis module is used to obtain the intervention information of the patient and calculate the metabolic analysis parameters of the urine volume time series based on the intervention information;

[0125] 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 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 real-time urine volume of the patient based on the analysis feedback method.

[0126] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those skilled in the art to which the present invention pertains.

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; 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; is 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; 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, a floating age range of the target age is obtained, and all ages included in the floating age range are respectively input as keywords into a urine-related multidimensional information database for retrieval, and the retrieval results of different ages are merged to obtain target data, and the target data is subjected to a first data processing to obtain a weighted average of daily urine volume and a weighted average of urination frequency, and the weighted average of daily urine volume and the weighted average of urination frequency are respectively used as a first standard value and a 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; Obtain the characteristic urine volume corresponding to the offspring time series. The characteristic urine volume can be based on the integral part of the calculation formula of the first analysis parameter in S3. get; 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; 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: 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.

3. An intelligent urine flow monitoring and feedback method according to claim 2, 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.

4. The intelligent urine flow monitoring and feedback method according to claim 3, 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.

5. An intelligent urine flow monitoring and feedback method according to claim 4, 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.

6. An intelligent urine flow monitoring and feedback system, using an intelligent urine flow monitoring and feedback method as claimed in claim 5, 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.

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