Urinary post-operation sign monitoring data processing method and system

Abstract

The invention belongs to the technical field of data processing, and discloses a urinary postoperative sign monitoring data processing method and system. A urinary data set and a patient data set are collected and preprocessed, the urinary data set comprises character data, index data and color data, and the patient data set comprises heart rate data, blood pressure data and respiration data; processing the urinary data set and the patient data set to obtain state reference values, and classifying the state reference values to obtain a patient state classification result; processing the patient state classification result to obtain a state response decision result; the health short message is displayed through the display end, the urinary data set and the patient data set are analyzed, and an analysis result is obtained and displayed; generally speaking, the system has the remarkable advantages of being high in health data processing capacity, high in decision-making assistance and timely in sign monitoring data feedback.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method and system for processing post-urology vital sign monitoring data. Background Art

[0002] The main contents of urological surgery include kidney transplantation, laparoscopic surgery, adrenal surgical treatment of adrenal adenoma, cervical cell tumor, primary aldosteronism, etc., surgery of kidney, bladder and prostate tumors, prostate cancer surgery, surgery of ureteropelvic junction stenosis, surgical treatment of kidney, ureter and bladder stones, transvesical and retropubic prostatic hyperplasia removal surgery, transurethral bladder tumor electroresection, bladder tumor resection using holmium laser through cystoscope, hypospadias, penile flexion plastic surgery and other surgeries, extracorporeal lithotripsy for the treatment of kidney, ureter and bladder stones. Thanks to the rapid economic development of my country in recent years and the continuous improvement of the medical industry, postoperative physical sign monitoring of urology, as an important part of urological surgery, has also made great progress. However, postoperative physical sign monitoring of urology requires large data analysis and calculation, while traditional manual monitoring is prone to problems of poor accuracy and timeliness.

[0003] The patent application publication number CN118507075B discloses a method and system for processing urological postoperative vital sign monitoring data. By combining the patient's basic physical data and drinking water data, the collected vital sign monitoring data is comprehensively analyzed, and based on the individual differences of the patients, the basic physical data and drinking water data are used for fusion analysis and personalized analysis to improve the comprehensiveness and precision of data processing. By dynamically analyzing multiple groups of vital sign parameter sequences in the vital sign data set, the patient's postoperative status is monitored, and timely adjustments are made according to data fluctuations to avoid potential abnormalities from being concealed, thereby improving the accuracy of abnormal detection of urological postoperative vital sign monitoring data, thereby ensuring the quality of postoperative care and rehabilitation effects of patients.

[0004] However, although the above-mentioned method and system for processing data for monitoring vital signs after urology surgery ensures the accuracy of data abnormality detection to a certain extent by collecting the patient's basic physical data and water drinking data and analyzing them based on the individual differences of the patients, it is difficult to ensure the timeliness and decision-making assistance of the data, which are extremely important in monitoring vital signs after urology surgery, which is seriously related to the patient's physical condition and postoperative recovery.

[0005] In view of this, the present invention proposes a method and system for processing urological postoperative vital sign monitoring data to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: comprising:

[0007] The data collection module is used to collect urinary data sets and patient data sets and perform preprocessing. The urinary data sets include trait data, index data and color data, and the patient data sets include heart rate data, blood pressure data and respiratory data;

[0008] Furthermore, the method of collecting the urology dataset and the patient dataset includes:

[0009] By installing a urine analyzer, the turbidity coefficient, biochemical index coefficient and color coefficient of the collected patient's urine are analyzed respectively to obtain property data, index data and color data;

[0010] By installing a heart rate sensor, the patient's heart rate value is collected to obtain heart rate data;

[0011] By installing a blood pressure detector, the patient's blood pressure value is collected to obtain blood pressure data;

[0012] By installing a respiratory sensor, the patient's respiratory rate value is collected to obtain respiratory data;

[0013] The preprocessing methods include data cleaning and data denoising;

[0014] A data processing module is used to process the urinary data set and the patient data set to obtain a state reference value, and to classify the data to obtain a patient state classification result;

[0015] Further, the steps of processing the urology dataset and the patient dataset include:

[0016] Q1: The postoperative reference value is obtained by substituting the calculation formula: Aa = Ab × A1 + Ac × A2 + Ad × A3, where Ab is the trait data, Ac is the index data, Ad is the color data, and A1, A2 and A3 are the corresponding weight factors respectively;

[0017] Q2: Obtain the physiological reference value by substituting into the calculation formula: Ba = Bb × B1 + Bc × B2 + Bd × B3, where Bb is the heart rate data, Bc is the blood pressure data, Bd is the respiration data, and B1, B2 and B3 are the corresponding weight factors respectively; preset the physiological threshold interval (Y1, Y2), when the physiological reference value is greater than Y1 and less than Y2, the value is 1, when the physiological reference value is less than Y1 or greater than Y2, the value is 2;

[0018] Q3: Collect K groups of historical feature vectors as sample sets, and divide the sample sets into 70%K training sets, 15%K test sets, and 15%K validation sets;

[0019] Q4: Establish a state classification model based on the sample set, obtain the historical feature vectors in the training set, preset the normal data cluster L1, alarm data cluster L2 and warning data cluster L3, and randomly select three data points in the training set as the center of the first cluster, representing the higher normal data cluster L1, alarm data cluster L2 and warning data cluster L3 respectively;

[0020] Q5: By substituting into the calculation formula: Get the distance between data items, calculate the distance between the data items in the training set and the higher data cluster P1, normal data cluster P2 and lower data cluster P3 respectively, and assign the data items to the data clusters with the closest distance, and get three new data clusters as the second cluster center, where x and y are the coordinate values ​​of the data points, xi and yi are the values ​​of the two data points on the i sub-data items respectively, and n is the number of sub-data items;

[0021] Q6: Calculate the means of the three new data clusters in the second cluster center respectively, and calculate them again as the new first cluster center;

[0022] Q7: Repeat R5 and R6 until the preset number of iterations is reached to obtain the patient status classification model;

[0023] Q8: Input the feature vector into the patient status classification model and output the status reference value;

[0024] Further, the classification methods include:

[0025] A patient status threshold interval (E1, E2) is preset. When the status reference value is less than E1, a healthy signal is generated. When the status reference value is greater than E1 and less than E2, an abnormal signal is generated. When the status reference value is greater than E2, a warning signal is generated.

[0026] The health signal includes a group of fields representing that the patient's physical status data is normal, the abnormal signal includes a group of fields representing that the patient's physical status data is abnormal, and the warning signal includes a group of fields representing that the patient's physical status data is seriously abnormal;

[0027] Pack healthy signals, abnormal signals and warning signals to obtain patient status classification results;

[0028] The early warning feedback module is used to process the patient status classification results and obtain the status response decision results;

[0029] Furthermore, the method of processing the patient status classification result includes:

[0030] When the patient status classification result is a healthy signal, a healthy SMS is sent to the display end through the communication unit; when the patient status classification result is an abnormal signal, an abnormal SMS is sent to the medical staff receiving end through the communication unit; when the patient status classification result is a warning signal, a warning SMS is sent to the medical staff receiving end through the communication unit;

[0031] Health messages include information about the patient's good physical condition, reminding medical staff to provide regular checkups and care for the patient and to ensure that the patient is in a positive and optimistic state of mind;

[0032] The abnormal text message includes an explanation of the abnormality of the patient's physical condition, requiring medical personnel to immediately check and evaluate the patient's urinary data set and patient data set, and adjust the patient's drug dosage, change external medications, or perform pain management based on the examination results;

[0033] The warning text message includes a statement that the patient's physical condition is seriously abnormal, requiring medical personnel to immediately provide treatment, adopt life support measures, and discuss treatment plans;

[0034] Pack healthy SMS, abnormal SMS and warning SMS to get the status response decision results;

[0035] The user interaction module is used to display the health text message through the display terminal, analyze the urinary data set and the patient data set, and obtain and display the analysis results;

[0036] Further, the urology dataset and the patient dataset are analyzed by:

[0037] A data standard threshold interval group is preset, and the urinary data set and the patient data set are respectively substituted into the corresponding data standard threshold interval for comparison, and the healthy state, abnormal state or warning state is output;

[0038] When both the urology data set and the patient data set are healthy results, the healthy state is output; when one or more of the urology data set and the patient data set are abnormal results and the rest are healthy results, the abnormal state is output; when one or more of the urology data set and the patient data set are warning results and the rest are healthy results or abnormal results, the warning state is output;

[0039] Pack the health status, abnormal status and warning status to get the analysis results;

[0040] Further, S1: collecting a urinary data set and a patient data set, and performing preprocessing, the urinary data set includes trait data, index data and color data, and the patient data set includes heart rate data, blood pressure data and respiratory data;

[0041] S2: Process the urinary data set and the patient data set to obtain a state reference value, and classify them to obtain a patient state classification result;

[0042] S3: Process the patient status classification results to obtain status response decision results;

[0043] S4: Display the health message through the display terminal, analyze the urology data set and the patient data set, and obtain and display the analysis results.

[0044] The technical effects and advantages of the method and system for processing urological postoperative vital sign monitoring data of the present invention are as follows:

[0045] The present invention processes the urinary data set and the patient data set to obtain a state reference value that can directly reflect the patient's current physical state, greatly reducing the huge workload of medical personnel due to analyzing and calculating health data. By processing the patient state classification results, the state response decision results obtained can assist medical personnel in quickly implementing highly targeted response measures according to the different physical states of the patients, greatly reducing the risks brought about by emergencies. The analysis results obtained by analyzing the urinary data set and the patient data set can assist medical personnel in quickly screening the patient's single health data to avoid health risks caused by the failure to respond in time to the excessive single health data of the patient. Generally speaking, the present invention has the significant advantages of strong health data processing capabilities, high decision-making assistance and timely feedback of physical sign monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of a post-urology vital sign monitoring data processing system of the present invention;

[0047] Figure 2 It is a schematic diagram of a method for processing data of post-urology vital sign monitoring according to the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] See also Figure 1 As shown, the urology postoperative vital sign monitoring data processing system described in this embodiment includes:

[0051] The data collection module is used to collect urinary data sets and patient data sets and perform preprocessing. The urinary data sets include trait data, index data and color data, and the patient data sets include heart rate data, blood pressure data and respiratory data;

[0052] Furthermore, the method of collecting the urology dataset and the patient dataset includes:

[0053] By installing a urine analyzer, the turbidity coefficient, biochemical index coefficient and color coefficient of the collected patient's urine are analyzed respectively to obtain property data, index data and color data;

[0054] It should be explained that the turbidity coefficient is the turbidity of the patient's urine, and the values ​​are 1, 2 and 3 according to the three degrees of clarity, impurities and turbidity respectively; the biochemical index coefficient is obtained by analyzing the urine protein, uric acid and urine sugar values ​​in the patient's urine and performing weighted summation. Through the preset index threshold interval (W1, W2), when the biochemical index coefficient is less than W1, the value is 1, when it is greater than W1 and less than W2, the value is 2, and when it is greater than W2, the value is 1; the color coefficient is obtained by comparing the color of the patient's urine with the color of the standard urine. According to the specific classification of the standard urine color, the values ​​are integers of 1-4 from low to high;

[0055] By installing a heart rate sensor, the patient's heart rate value is collected to obtain heart rate data;

[0056] By installing a blood pressure detector, the patient's blood pressure value is collected to obtain blood pressure data;

[0057] By installing a respiratory sensor, the patient's respiratory rate value is collected to obtain respiratory data;

[0058] The preprocessing methods include data cleaning and data denoising;

[0059] A data processing module is used to process the urinary data set and the patient data set to obtain a state reference value, and to classify the data to obtain a patient state classification result;

[0060] Further, the steps of processing the urology dataset and the patient dataset include:

[0061] Q1: The postoperative reference value is obtained by substituting the calculation formula: Aa = Ab × A1 + Ac × A2 + Ad × A3, where Ab is the trait data, Ac is the index data, Ad is the color data, and A1, A2 and A3 are the corresponding weight factors respectively;

[0062] Q2: Obtain the physiological reference value by substituting into the calculation formula: Ba = Bb × B1 + Bc × B2 + Bd × B3, where Bb is the heart rate data, Bc is the blood pressure data, Bd is the respiration data, and B1, B2 and B3 are the corresponding weight factors respectively; preset the physiological threshold interval (Y1, Y2), when the physiological reference value is greater than Y1 and less than Y2, the value is 1, when the physiological reference value is less than Y1 or greater than Y2, the value is 2;

[0063] Q3: Collect K groups of historical feature vectors as sample sets, and divide the sample sets into 70%K training sets, 15%K test sets, and 15%K validation sets;

[0064] It should be explained that the eigenvector is the sum of the postoperative reference value and the physiological reference value;

[0065] Q4: Establish a state classification model based on the sample set, obtain the historical feature vectors in the training set, preset the normal data cluster L1, alarm data cluster L2 and warning data cluster L3, and randomly select three data points in the training set as the center of the first cluster, representing the higher normal data cluster L1, alarm data cluster L2 and warning data cluster L3 respectively;

[0066] Q5: By substituting into the calculation formula: Get the distance between data items, calculate the distance between the data items in the training set and the higher data cluster P1, normal data cluster P2 and lower data cluster P3 respectively, and assign the data items to the data clusters with the closest distance, and get three new data clusters as the second cluster center, where x and y are the coordinate values ​​of the data points, xi and yi are the values ​​of the two data points on the i sub-data items respectively, and n is the number of sub-data items;

[0067] Q6: Calculate the means of the three new data clusters in the second cluster center respectively, and calculate them again as the new first cluster center;

[0068] Q7: Repeat R5 and R6 until the preset number of iterations is reached to obtain the patient status classification model;

[0069] Q8: Input the feature vector into the patient status classification model and output the status reference value;

[0070] Further classification methods include:

[0071] A patient status threshold interval (E1, E2) is preset. When the status reference value is less than E1, a healthy signal is generated. When the status reference value is greater than E1 and less than E2, an abnormal signal is generated. When the status reference value is greater than E2, a warning signal is generated.

[0072] The health signal includes a group of fields representing that the patient's physical status data is normal, the abnormal signal includes a group of fields representing that the patient's physical status data is abnormal, and the warning signal includes a group of fields representing that the patient's physical status data is seriously abnormal;

[0073] Pack healthy signals, abnormal signals and warning signals to obtain patient status classification results;

[0074] The early warning feedback module is used to process the patient status classification results and obtain the status response decision results;

[0075] Further, the method of processing the patient status classification result includes:

[0076] When the patient status classification result is a healthy signal, a healthy SMS is sent to the display end through the communication unit; when the patient status classification result is an abnormal signal, an abnormal SMS is sent to the medical staff receiving end through the communication unit; when the patient status classification result is a warning signal, a warning SMS is sent to the medical staff receiving end through the communication unit;

[0077] Health messages include information about the patient's good physical condition, reminding medical staff to provide regular checkups and care for the patient and to ensure that the patient is in a positive and optimistic state of mind;

[0078] The abnormal text message includes an explanation of the abnormality of the patient's physical condition, requiring medical personnel to immediately check and evaluate the patient's urinary data set and patient data set, and adjust the patient's drug dosage, change external medications, or perform pain management based on the examination results;

[0079] The warning text message includes a statement that the patient's physical condition is seriously abnormal, requiring medical personnel to immediately provide treatment, adopt life support measures, and discuss treatment plans;

[0080] Pack healthy SMS, abnormal SMS and warning SMS to get the status response decision results;

[0081] The user interaction module is used to display the health text message through the display terminal, analyze the urinary data set and the patient data set, and obtain and display the analysis results;

[0082] Further, the methods for analyzing the urology dataset and the patient dataset include:

[0083] A data standard threshold interval group is preset, and the urinary data set and the patient data set are respectively substituted into the corresponding data standard threshold interval for comparison, and the healthy state, abnormal state or warning state is output;

[0084] It should be explained that the data standard threshold interval group is the threshold intervals set manually for trait data, indicator data, color data, heart rate data, blood pressure data and respiratory data, all of which are implemented in a multi-interval manner. For example, if the trait threshold interval is (R1, R2), when the trait data is less than R1, it is a healthy result, greater than R1 and less than R2 is an abnormal result, and greater than R2 is a warning result;

[0085] When both the urology data set and the patient data set are healthy results, the healthy state is output; when one or more of the urology data set and the patient data set are abnormal results and the rest are healthy results, the abnormal state is output; when one or more of the urology data set and the patient data set are warning results and the rest are healthy results or abnormal results, the warning state is output;

[0086] Pack the health status, abnormal status and warning status to get the analysis results;

[0087] The beneficial effects of this embodiment are as follows: by processing the urinary data set and the patient data set, the state reference value obtained can directly reflect the patient's current physical state, greatly reducing the huge workload of medical personnel due to the analysis and calculation of health data; by processing the patient state classification results, the state response decision results obtained can assist medical personnel in quickly implementing highly targeted response measures according to the different physical states of the patients, greatly reducing the risks brought about by emergencies; by analyzing the urinary data set and the patient data set, the analysis results obtained can assist medical personnel in quickly screening the patient's single health data, avoiding the health risks caused by the failure to respond in time to the excessive single health data of the patient; in general, the present invention has the significant advantages of strong health data processing capabilities, high decision-making assistance and timely feedback of vital sign monitoring data.

[0088] Example 2

[0089] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a method for processing urological postoperative physical sign monitoring data is provided, comprising: S1: collecting a urological data set and a patient data set, and performing preprocessing, the urological data set comprising trait data, index data and color data, and the patient data set comprising heart rate data, blood pressure data and respiratory data;

[0090] S2: Process the urinary data set and the patient data set to obtain a state reference value, and classify them to obtain a patient state classification result;

[0091] S3: Process the patient status classification results to obtain status response decision results;

[0092] S4: Display the health message through the display terminal, analyze the urology data set and the patient data set, and obtain and display the analysis results.

[0093] Example 3

[0094] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned method for processing urology postoperative vital sign monitoring data is implemented.

[0095] Since the electronic device introduced in this embodiment is an electronic device used to implement a method for processing data after urinary surgery for monitoring physical signs in the embodiment of this application, based on the method for processing data after urinary surgery for monitoring physical signs in the embodiment of this application, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as a person skilled in the art implements the electronic device used in the method for processing data after urinary surgery for monitoring physical signs in the embodiment of this application, it belongs to the scope of protection of this application.

[0096] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0097] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A post-urology vital sign monitoring data processing system, characterized in that: include: The data collection module is used to collect urinary data sets and patient data sets and perform preprocessing. The urinary data sets include trait data, index data and color data, and the patient data sets include heart rate data, blood pressure data and respiratory data; A data processing module is used to process the urinary data set and the patient data set to obtain a state reference value, and to classify the data to obtain a patient state classification result; The early warning feedback module is used to process the patient status classification results and obtain the status response decision results; The user interaction module is used to display the health text messages through the display terminal, analyze the urinary data set and the patient data set, and obtain and display the analysis results.

2. A urology postoperative vital sign monitoring data processing system according to claim 1, characterized in that: Ways to collect urology datasets and patient datasets include: By installing a urine analyzer, the turbidity coefficient, biochemical index coefficient and color coefficient of the collected patient's urine are analyzed respectively to obtain property data, index data and color data; By installing a heart rate sensor, the patient's heart rate value is collected to obtain heart rate data; By installing a blood pressure detector, the patient's blood pressure value is collected to obtain blood pressure data; By installing a respiratory sensor, the patient's respiratory rate value is collected to obtain respiratory data; The preprocessing methods include data cleaning and data denoising.

3. A urology postoperative vital sign monitoring data processing system according to claim 2, characterized in that: The steps for processing the urology dataset and patient dataset include: Q1: The postoperative reference value is obtained by substituting the calculation formula: Aa = Ab × A1 + Ac × A2 + Ad × A3, where Ab is the trait data, Ac is the index data, Ad is the color data, and A1, A2 and A3 are the corresponding weight factors respectively; Q2: Obtain the physiological reference value by substituting into the calculation formula: Ba = Bb × B1 + Bc × B2 + Bd × B3, where Bb is the heart rate data, Bc is the blood pressure data, Bd is the respiration data, and B1, B2 and B3 are the corresponding weight factors respectively; preset the physiological threshold interval (Y1, Y2), when the physiological reference value is greater than Y1 and less than Y2, the value is 1, when the physiological reference value is less than Y1 or greater than Y2, the value is 2; Q3: Collect K groups of historical feature vectors as sample sets, and divide the sample sets into 70%K training sets, 15%K test sets, and 15%K validation sets; Q4: Establish a state classification model based on the sample set, obtain the historical feature vectors in the training set, preset the normal data cluster L1, alarm data cluster L2 and warning data cluster L3, and randomly select three data points in the training set as the center of the first cluster, representing the higher normal data cluster L1, alarm data cluster L2 and warning data cluster L3 respectively; Q5: By substituting into the calculation formula: Get the distance between data items, calculate the distance between the data items in the training set and the higher data cluster P1, normal data cluster P2 and lower data cluster P3 respectively, and assign the data items to the data clusters with the closest distance, and get three new data clusters as the second cluster center, where x and y are the coordinate values ​​of the data points, xi and yi are the values ​​of the two data points on the i sub-data items respectively, and n is the number of sub-data items; Q6: Calculate the means of the three new data clusters in the second cluster center respectively, and calculate them again as the new first cluster center; Q7: Repeat R5 and R6 until the preset number of iterations is reached to obtain the patient status classification model; Q8: Input the feature vector into the patient status classification model and output the status reference value.

4. A urology postoperative vital sign monitoring data processing system according to claim 3, characterized in that: The classification methods include: A patient status threshold interval (E1, E2) is preset. When the status reference value is less than E1, a healthy signal is generated. When the status reference value is greater than E1 and less than E2, an abnormal signal is generated. When the status reference value is greater than E2, a warning signal is generated. The health signal includes a group of fields representing that the patient's physical status data is normal, the abnormal signal includes a group of fields representing that the patient's physical status data is abnormal, and the warning signal includes a group of fields representing that the patient's physical status data is seriously abnormal; Pack healthy signals, abnormal signals and warning signals to obtain patient status classification results.

5. A urology postoperative vital sign monitoring data processing system according to claim 4, characterized in that: Methods for processing patient status classification results include: When the patient status classification result is a healthy signal, a healthy SMS is sent to the display end through the communication unit; when the patient status classification result is an abnormal signal, an abnormal SMS is sent to the medical staff receiving end through the communication unit; when the patient status classification result is a warning signal, a warning SMS is sent to the medical staff receiving end through the communication unit; Health messages include information about the patient's good physical condition, reminding medical staff to provide regular checkups and care for the patient and to ensure that the patient is in a positive and optimistic state of mind; The abnormal text message includes an explanation of the abnormality of the patient's physical condition, requiring medical personnel to immediately check and evaluate the patient's urinary data set and patient data set, and adjust the patient's drug dosage, change external medications, or perform pain management based on the examination results; The warning text message includes a statement that the patient's physical condition is seriously abnormal, requiring medical personnel to immediately provide treatment, adopt life support measures, and discuss treatment plans; Pack healthy text messages, abnormal text messages and warning text messages to get the status response decision results.

6. A urology postoperative vital sign monitoring data processing system according to claim 5, characterized in that: The urology dataset and patient dataset were analyzed by: A data standard threshold interval group is preset, and the urinary data set and the patient data set are respectively substituted into the corresponding data standard threshold interval for comparison, and the healthy state, abnormal state or warning state is output; When both the urology data set and the patient data set are healthy results, the healthy state is output; when one or more of the urology data set and the patient data set are abnormal results and the rest are healthy results, the abnormal state is output; when one or more of the urology data set and the patient data set are warning results and the rest are healthy results or abnormal results, the warning state is output; Pack the health status, abnormal status and warning status to get the analysis results.

7. A method for processing post-urology vital sign monitoring data, implemented by any one of the post-urology vital sign monitoring data processing systems described in claim 1-6, characterized in that: S1: Collect urinary data sets and patient data sets and perform preprocessing. The urinary data sets include trait data, index data and color data, and the patient data sets include heart rate data, blood pressure data and respiratory data; S2: Process the urinary data set and the patient data set to obtain a state reference value, and classify them to obtain a patient state classification result; S3: Process the patient status classification results to obtain status response decision results; S4: Display the health message through the display terminal, analyze the urology data set and the patient data set, and obtain and display the analysis results.

Images