Urinary post-operation sign monitoring data processing method and system

Abstract

The invention discloses a urinary postoperative sign monitoring data processing method and system, and relates to the technical field of medical monitoring, and the method comprises the steps: carrying out the comprehensive analysis of the postoperative sign data of each patient through constructing a preliminary dehydration risk algorithm model in a cloud data processing platform, and generating a preliminary dehydration risk index DS; the index is calculated based on multiple key sign data. Through accurate modeling and analysis of the data, the dehydration risk of each patient can be quantified, and preliminary screening is carried out according to the numerical value of the dehydration risk index DS. In the preliminary evaluation stage, if the preliminary dehydration risk index DS value of the patient is larger than or equal to 0.05, the patient is prompted to have the dehydration risk, and deep dehydration and edema analysis is carried out on the patient. According to the accurate evaluation mode based on data, the dehydration risk patient can be found in the early stage after the operation, an important basis is provided for subsequent intervention and treatment, and potential health hidden dangers are reduced.

Description

Technical Field

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

[0002] Urological postoperative care is an important branch in the field of modern medicine, focusing on the recovery and health management of postoperative patients. Especially after urological surgery, the patient's water and electrolyte balance, body fluid management, and renal function monitoring are particularly critical. With the advancement of medical technology and the diversification of surgical methods, the postoperative recovery management of patients after urological surgery is becoming increasingly complex. In this process, postoperative vital sign monitoring has become an important part of medical care, including real-time monitoring of physiological parameters such as urine discharge, heart rate, and total body water content. Traditional manual monitoring methods often fail to detect problems such as postoperative edema or dehydration in a timely and accurate manner because they rely on the experience of medical staff.

[0003] At present, the monitoring of vital signs of patients after urological surgery mainly relies on traditional manual inspection or intermittent monitoring, which lacks continuity and real-time performance. For example, changes in body fluid status such as dehydration or edema are often discovered only when the patient's symptoms are obvious, resulting in untimely clinical intervention and possible postoperative complications. Although some traditional equipment such as weight scales and urine analyzers can monitor patients' urine output and urine osmotic pressure, they cannot comprehensively and comprehensively evaluate the dynamic changes of patients' body fluids. In addition, existing monitoring equipment usually lacks integration, resulting in fragmented data, making it difficult to achieve effective real-time monitoring and intelligent analysis, increasing the workload of medical staff, and there is a risk of data analysis lag and error. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for processing urological postoperative vital sign monitoring data, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: comprising the following steps:

[0006] S1. Equip all patients after urological surgery with a sensor group to collect postoperative vital sign data of all patients in real time, build a cloud data processing platform, and transmit the collected postoperative vital sign data to the cloud data processing platform through the Internet of Things;

[0007] S2. Preprocessing the postoperative vital sign data in the cloud data processing platform, obtaining standard vital sign data of each patient, marking and numbering the standard vital sign data of each patient, and setting up a cloud database, storing the standard vital sign data in the cloud database, and sorting them in order of numbers;

[0008] S3. Construct a preliminary dehydration risk algorithm model in the cloud data processing platform, extract the standard physical sign data of each patient and input them into the preliminary dehydration risk algorithm model respectively, calculate and output the preliminary dehydration risk index DS of each patient, perform preliminary comparative evaluation based on the output results of the preliminary dehydration risk index DS, and screen patients with dehydration risk;

[0009] S4. For patients who are screened out as having a risk of dehydration through preliminary assessment, perform a deep dehydration and edema analysis, wherein the deep dehydration and edema analysis extracts standard physical sign data, calculates and outputs a deep dehydration risk index DA and an edema risk index EA, and then comprehensively calculates the deep dehydration risk index DA and the edema risk index EA to output a comprehensive risk index R;

[0010] S5. Perform a secondary comparative evaluation based on the output results of the comprehensive risk index R, analyze the current patient's dehydration and edema risks, generate warning information and send it to the client to push warning notifications.

[0011] Preferably, said S1 includes S11 and S12;

[0012] S11, equipping all patients after urology surgery with a sensor group, and setting the acquisition frame rate of the sensor group to once per minute, so as to collect postoperative vital sign data of all patients after urology surgery in real time;

[0013] The sensor group includes a urine analyzer, a smart weight scale, a blood oxygen saturation detector, an electrocardiogram device, a limb dimension measuring instrument, and a bioelectrical impedance analyzer;

[0014] The postoperative physical sign data include urination rate Pdi, urine osmotic pressure UOsm, heart rate variability index HRV, heart rate response coefficient RHR, total body water content TBW and limb swelling EL;

[0015] S12. Through the communication module in the sensor group, use the MQTT protocol of the Internet of Things to encapsulate the postoperative vital sign data, obtain the vital sign data packet, and generate a unique identifier for each vital sign data packet, and then transmit the vital sign data packet to the cloud data processing platform through the wireless communication network.

[0016] Preferably, S2 includes S21 and S22;

[0017] S21. Receive the vital sign data packet in real time in the cloud data processing platform, unseal the vital sign data packet, extract the postoperative vital sign data, and set an API application program interface to integrate the cloud data processing platform with the case system, match the patient case information in the case library based on the unique identifier, and extract the patient's initials and the last four digits of the ID card;

[0018] After matching the patient case information, preprocessing is performed on the postoperative vital sign data of all patients, and the preprocessing includes abnormal data elimination and data standardization, obtaining standard vital sign data, and labeling the standard vital sign data of each patient with a number;

[0019] The number is composed in the format of: initials of name / last four digits of ID card / year, month, day / last two digits of unique identifier;

[0020] The abnormal data elimination is carried out by using the data anomaly detection algorithm z-score on the postoperative vital sign data to eliminate sensor failure and data deviation;

[0021] The data standardization is performed by denoising, filling missing values ​​and normalizing the postoperative vital sign data;

[0022] S22. At the same time, a cloud database is set up, and an API application program interface is set up to integrate the cloud data processing platform with the cloud database, store the standard vital signs data in the cloud database, and sort them in order of number.

[0023] Preferably, said S3 includes S31 and S32;

[0024] S31. Based on the machine learning algorithm, a preliminary dehydration risk algorithm model is constructed in the cloud data processing platform, and the standard vital sign data of each patient in the cloud database is extracted through the API application program interface, and input into the preliminary dehydration risk algorithm model to calculate and output the preliminary dehydration risk index DS of each patient, and comprehensively quantify the dehydration index of each patient;

[0025] The preliminary dehydration risk indicator DS is calculated and output by the following preliminary dehydration risk algorithm model;

[0026]

[0027] Where, DS i represents the initial dehydration risk index of the i-th patient, ln represents the natural logarithm, UOsm i represents the urine osmotic pressure of the i-th patient, Pdi i represents the urination rate of the i-th patient, TBW i represents the total body water content of the i-th patient, HRV i Represents the heart rate variability index of the i-th patient, RHR i represents the heart rate response coefficient of the i-th patient, EL i represents the limb swelling of the i-th patient.

[0028] Preferably, S32, by calculating the preliminary dehydration risk index DS of each patient, and based on the output result of the preliminary dehydration risk index DS, performing a preliminary assessment to screen patients with dehydration risk, the specific assessment content is as follows;

[0029] When the initial dehydration risk index DS of the i-th patient i When ≥0.05, it means that the patient is at risk of dehydration, and deep dehydration and edema analysis is performed at this time;

[0030] When the initial dehydration risk index DS of the i-th patient i When <0.05, it means the patient is normal and needs to be monitored continuously.

[0031] Preferably, S4 includes S41, S42 and S43;

[0032] S41, after preliminary screening of patients at risk of dehydration, further performing deep dehydration and edema analysis, wherein the deep dehydration and edema analysis includes deep dehydration analysis and deep edema analysis;

[0033] The deep dehydration analysis uses a nonlinear power function and an exponential function, combined with urine osmotic pressure UOsm, total body water content TBW, urination rate Pdi, heart rate variability index HRV and limb swelling EL, to calculate and output a deep dehydration risk index DA for each screened patient;

[0034] The deep dehydration risk index DA is calculated and output by the following algorithm formula:

[0035]

[0036] In the formula, DA i represents the deep dehydration risk index of the i-th patient, exp represents the exponential function, and HRcrit represents the critical heart rate.

[0037] Preferably, S42, the deep edema analysis calculates and outputs the edema risk index EA by combining the nonlinear power relationship, the exponential function and the standard physical sign data, and analyzes the edema risk;

[0038] The edema risk index EA is calculated and output by the following algorithm formula;

[0039]

[0040] Where Pfl represents fluid intake, EA i represents the edema risk index EA of the i-th patient.

[0041] Preferably, S43, based on the acquired deep dehydration risk index DA and edema risk index EA, a comprehensive calculation is performed to obtain a comprehensive analysis index R, and the postoperative risk of the patient is analyzed;

[0042] The comprehensive analysis index R is calculated and output by the following algorithm formula;

[0043]

[0044] Where α and β represent the deep dehydration risk index DA of the i-th patient, respectively. i and the edema risk index EA for the i-th patient i The weight value is α+β=1, and the specific value is set by the user.

[0045] Preferably, said S5 includes S51 and S52;

[0046] S51. Based on the output results of the comprehensive analysis index R, a secondary comparative evaluation is conducted to analyze the dehydration and edema of the current postoperative urological patients, and intervention recommendations are generated based on the secondary comparative evaluation results. The specific evaluation contents are as follows;

[0047] When the comprehensive analysis index R>1, it means that the current patient is at risk of dehydration, and the first intervention recommendation is generated;

[0048] When the comprehensive analysis index R=1, it means that the patient's current risk of dehydration and edema is normal, and there is no need for intervention and continuous testing;

[0049] When the comprehensive analysis index R < 1, it means that the current patient is at risk of edema, and the second intervention recommendation is generated;

[0050] S52, based on the obtained intervention suggestions, generating early warning information and pushing it to the client through the cloud data processing platform to prompt medical staff to intervene in the patient after urology surgery, wherein the client includes a mobile terminal and a nursing terminal;

[0051] The specific warning information is as follows:

[0052] When the first intervention suggestion is generated, the first warning message is pushed to remind medical staff that the patient is at risk of dehydration. At this time, the patient should increase the fluid supplement by 30%, adjust the drug dosage, and switch to intravenous fluid replacement.

[0053] When the second intervention suggestion is generated, a second warning message is pushed to remind medical staff that the patient is at risk of edema. At this time, the medication is adjusted and the patient is advised to exercise.

[0054] A post-operative urology vital sign monitoring data processing system, comprising a post-operative vital sign collection module, a cloud processing module, a preliminary dehydration analysis module, a deep dehydration and edema analysis module and a comprehensive evaluation push module;

[0055] The postoperative vital sign collection module collects the postoperative vital sign data of all patients in real time by equipping all patients after urological surgery with a sensor group, and builds a cloud data processing platform to transmit the collected postoperative vital sign data to the cloud data processing platform through the Internet of Things;

[0056] The cloud processing module obtains the standard vital sign data of each patient by preprocessing the postoperative vital sign data in the cloud data processing platform, and marks and numbers the standard vital sign data of each patient, and sets a cloud database, stores the standard vital sign data in the cloud database, and sorts them in the order of numbers;

[0057] The preliminary dehydration analysis module constructs a preliminary dehydration risk algorithm model in the cloud data processing platform, extracts the standard physical sign data of each patient and inputs them into the preliminary dehydration risk algorithm model respectively, calculates and outputs a preliminary dehydration risk index DS for each patient, and performs preliminary comparative evaluation based on the output results of the preliminary dehydration risk index DS to screen patients with dehydration risks;

[0058] The deep dehydration and edema analysis module screens out patients with dehydration risks through preliminary assessment, and performs deep dehydration and edema analysis. The deep dehydration and edema analysis extracts standard physical sign data, calculates and outputs a deep dehydration risk index DA and an edema risk index EA, and then comprehensively calculates the deep dehydration risk index DA and the edema risk index EA to output a comprehensive risk index R;

[0059] The comprehensive assessment push module performs a secondary comparative assessment based on the output results of the comprehensive risk index R, analyzes the dehydration and edema risks of the current patient, generates warning information and sends it to the client to push warning notifications.

[0060] The present invention provides a method and system for processing post-urology vital sign monitoring data. It has the following beneficial effects:

[0061] (1) This method assembles a sensor group and uses the Internet of Things technology to collect the vital sign data of all patients after urological surgery in real time, and transmits it to the cloud data processing platform through the MQTT protocol, thereby realizing real-time monitoring of postoperative vital signs. The vital sign data of each patient is automatically collected through multiple dedicated sensors to ensure the accuracy and timeliness of the data. In addition, the sensor group has its own communication module, which can encapsulate the vital sign data into a vital sign data packet and transmit it to the cloud data processing platform through a wireless network to ensure timely transmission and storage of the data. This real-time collection and processing mechanism greatly improves the accuracy of postoperative vital sign data, reduces the error of manual monitoring, helps to detect potential risk factors such as dehydration or edema as early as possible, and ensures that patients can receive more accurate and effective care after surgery.

[0062] (2) This method constructs a preliminary dehydration risk algorithm model on a cloud data processing platform, conducts a comprehensive analysis of each patient's postoperative vital signs data, and generates a preliminary dehydration risk index DS. The calculation of this index is based on multiple key vital signs data. Through accurate modeling and analysis of these data, the dehydration risk of each patient can be quantified, and preliminary screening can be carried out based on the value of the dehydration risk index DS. In the preliminary evaluation stage, if the patient's DS value is greater than or equal to 0.05, it indicates that the patient is at risk of dehydration, and a deep dehydration and edema analysis is performed on the patient. This data-based and precise evaluation method can detect patients at risk of dehydration in the early postoperative period, provide an important basis for subsequent intervention and treatment, and reduce potential health risks.

[0063] (3) After initially screening out patients at risk of dehydration, this method further performs deep dehydration and edema analysis to conduct a more refined risk assessment of patients. Deep dehydration analysis calculates the deep dehydration risk index DA by using nonlinear power functions and exponential functions. Edema risk analysis calculates the edema risk index EA by integrating nonlinear power relationships, exponential functions and other algorithms. On this basis, the deep dehydration risk index DA and the edema risk index EA are comprehensively calculated to obtain the comprehensive risk index R. According to the different values ​​of the comprehensive risk index R, the system automatically generates intervention suggestions. If the comprehensive risk index R value is greater than 1, it indicates a high risk of dehydration. The system will recommend increasing fluid supplementation and adjusting drug treatment; if the comprehensive risk index R value is less than 1, it indicates a high risk of edema. It is recommended to adjust the drug or increase the amount of exercise for intervention. Finally, the system will push the intervention suggestions to the client of medical staff through early warning information to ensure timely intervention. This risk assessment and intervention decision-making mechanism based on comprehensive analysis has greatly improved the management efficiency of patients' postoperative risks, and can quickly take effective measures when patients have problems, avoiding further deterioration of potential health risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of the steps of a method for processing post-urology vital sign monitoring data according to the present invention;

[0065] Figure 2 The present invention is a flow chart of a system for processing data of post-urology vital sign monitoring. DETAILED DESCRIPTION

[0066] 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.

[0067] Example 1

[0068] See also Figure 1 The present invention provides a method for processing post-urology vital sign monitoring data. To achieve the above purpose, the present invention is implemented through the following technical scheme: comprising the following steps:

[0069] S1. Equip all patients after urological surgery with a sensor set to collect all patients’ postoperative vital signs data in real time, build a cloud data processing platform, and transmit the collected postoperative vital signs data to the cloud data processing platform through the Internet of Things;

[0070] S2. Preprocessing the postoperative vital sign data in the cloud data processing platform, obtaining the standard vital sign data of each patient, and marking and numbering the standard vital sign data of each patient, and setting up a cloud database, storing the standard vital sign data in the cloud database, and sorting them in the order of numbers;

[0071] S3. Construct a preliminary dehydration risk algorithm model in the cloud data processing platform, extract the standard physical sign data of each patient and input them into the preliminary dehydration risk algorithm model respectively, calculate and output the preliminary dehydration risk index DS of each patient, conduct preliminary comparative evaluation based on the output results of the preliminary dehydration risk index DS, and screen patients with dehydration risk;

[0072] S4. Perform deep dehydration and edema analysis on patients who are screened out as having dehydration risk through preliminary assessment. The deep dehydration and edema analysis extracts standard physical sign data, calculates and outputs deep dehydration risk index DA and edema risk index EA, and then comprehensively calculates the deep dehydration risk index DA and edema risk index EA to output the comprehensive risk index R;

[0073] S5. Perform a secondary comparative evaluation based on the output results of the comprehensive risk index R, analyze the current patient's dehydration and edema risks, generate warning information and send it to the client to push warning notifications.

[0074] In this embodiment, the system realizes the whole process of real-time monitoring and risk assessment of postoperative patients through a series of precise steps, combined with Internet of Things technology, cloud data processing and algorithm analysis. The specific implementation method includes: first, assembling the sensor group for all patients after urological surgery, collecting postoperative vital signs data in real time, and transmitting the data to the cloud platform through the Internet of Things; then, preprocessing the data in the cloud platform, obtaining standard vital signs data and numbering and sorting and storing them; then, based on the standard vital signs data of each patient, constructing a preliminary dehydration risk algorithm model, calculating and outputting the preliminary dehydration risk index DS, and screening out dehydration risk patients through preliminary comparison; next, performing deep dehydration and edema analysis on the screened high-risk patients, calculating the deep dehydration risk index DA and edema risk index EA, and comprehensively calculating the comprehensive risk index R; finally, performing a secondary evaluation based on the output result of the comprehensive risk index R, generating early warning information and pushing it to the client, and promptly notifying medical staff to take intervention measures. Through the above steps, the present invention effectively improves the efficiency of collecting, storing and processing postoperative vital signs data, and ensures the real-time and accuracy of the data. Furthermore, through preliminary dehydration risk assessment and deep dehydration and edema analysis, patients at risk can be accurately screened, and risk assessment reports and intervention recommendations can be generated in real time. This method significantly improves the accuracy and real-time response capabilities of postoperative care, enabling medical staff to adjust treatment plans in a timely manner based on comprehensive analysis results, optimize the patient care process, reduce the incidence of postoperative complications, and improve the quality of postoperative recovery of patients. The implementation of this system not only enhances the decision-making support capabilities of medical staff, but also improves the safety and satisfaction of patients, thereby providing effective technical support for the health management of patients after urological surgery.

[0075] Example 2

[0076] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0077] S11, equipping all patients after urology surgery with a sensor group, and setting the acquisition frame rate of the sensor group to once per minute, so as to collect postoperative vital sign data of all patients after urology surgery in real time;

[0078] The sensor set includes a urine analyzer, a smart weight scale, a blood oxygen saturation monitor, an electrocardiogram device, a limb dimension measuring instrument, and a bioelectrical impedance analyzer;

[0079] Postoperative physical sign data included urination rate Pdi, urine osmotic pressure UOsm, heart rate variability index HRV, heart rate response coefficient RHR, total body water content TBW and limb swelling EL;

[0080] The urine excretion rate Pdi and urine osmotic pressure UOsm were collected and obtained by urine analyzer;

[0081] Heart rate variability index HRV and heart rate response coefficient RHR are acquired through electrocardiogram equipment;

[0082] The limb swelling degree EL is obtained by collecting data through a limb dimension measuring instrument;

[0083] Total body water content TBW is obtained by bioelectrical impedance analyzer;

[0084] S12. Through the communication module in the sensor group, use the MQTT protocol of the Internet of Things to encapsulate the postoperative vital sign data, obtain the vital sign data packet, and generate a unique identifier for each vital sign data packet, and then transmit the vital sign data packet to the cloud data processing platform through the wireless communication network.

[0085] In this embodiment, the method constructs an efficient and accurate system for monitoring the vital signs of patients after urological surgery. The specific implementation method includes: first, a complete set of sensor groups is equipped for each patient after urological surgery, and the acquisition frame rate is set to once per minute to collect the patient's postoperative vital signs data in real time. These sensor groups include urine analyzer, smart weight scale, blood oxygen saturation detector, electrocardiogram equipment, limb dimension measuring instrument and bioelectrical impedance analyzer, which are responsible for collecting postoperative vital signs data respectively. The collection of each data provides a comprehensive and high-precision basis for subsequent risk assessment and patient health monitoring. Next, through the communication module built into the sensor group, the MQTT protocol of the Internet of Things is used to encapsulate the collected vital signs data, generate a vital signs data packet, and transmit it to the cloud data processing platform through a wireless communication network. This process ensures the real-time and integrity of the data, and marks each data packet by generating a unique identifier, providing an efficient management means for the storage, processing and tracing of subsequent data. Through this implementation, the present invention realizes the efficient collection, real-time transmission and precise management of postoperative vital signs data. Its beneficial effects are mainly reflected in the following aspects: on the one hand, the use of advanced sensor technology and real-time data collection methods ensures the comprehensiveness and real-time nature of postoperative patient vital signs data, providing solid data support for subsequent health assessments; on the other hand, the application of Internet of Things technology makes data transmission more efficient, avoids delays caused by human intervention, and ensures the fast and accurate transmission of data.

[0086] Example 3

[0087] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0088] S21. Receive the vital sign data packet in real time in the cloud data processing platform, unseal the vital sign data packet, extract the postoperative vital sign data, and set an API application program interface to integrate the cloud data processing platform with the case system, match the patient case information in the case library based on the unique identifier, and extract the patient's initials and the last four digits of the ID card;

[0089] After matching the patient case information, preprocess the postoperative vital sign data of all patients. The preprocessing includes abnormal data removal and data standardization, obtaining standard vital sign data, and labeling the standard vital sign data of each patient with a number.

[0090] The number is in the following format: initials of name / last four digits of ID card / year, month, day / last two digits of unique identifier;

[0091] Abnormal data elimination: Sensor failure and data deviation are eliminated by using the data anomaly detection algorithm z-score on the postoperative vital sign data;

[0092] Data standardization was performed by denoising, filling missing values, and normalizing the postoperative vital sign data;

[0093] S22. At the same time, a cloud database is set up, and an API application program interface is set up to integrate the cloud data processing platform with the cloud database, store the standard vital signs data in the cloud database, and sort them in order of number.

[0094] In this embodiment, the method is closely integrated with the case system through the cloud data processing platform, and receives and unseals the vital sign data packet of each patient in real time through the API interface to extract the postoperative vital sign data. At the same time, the patient information in the case library is matched by the unique identifier, the initials of the patient's name and the last four digits of the ID card are extracted, and the vital sign data of each patient is given a unique identity. In terms of data preprocessing, an abnormal data elimination method is adopted, and sensor failures and data deviations are eliminated through data anomaly detection algorithms such as z-score to ensure the validity and accuracy of the data. In addition, the data is standardized, including denoising, filling missing values ​​and normalization, to further improve the consistency and comparability of the data. The cloud platform and the cloud database realize integrated connection, and the postoperative vital sign data after preprocessing and standardization are stored in the cloud database, and sorted according to the preset numbering format. The vital sign data of each patient is marked by an accurate numbering system, so that the management of the data is more standardized, which is convenient for subsequent query and analysis. Through this embodiment, the present invention realizes the accurate storage and standardized management of postoperative vital sign data. Its beneficial effects are mainly reflected in the following aspects: First, through data matching and labeling numbers, it ensures that the vital signs data of each patient can be accurately associated with the patient's identity information, which facilitates data traceability and management; second, the abnormal elimination and standardized processing of data effectively improve the quality and reliability of the data, and avoid misjudgment caused by sensor failure or data deviation; finally, through the efficient storage and sorting of cloud databases, the retrieval efficiency and access convenience of data are further improved, laying a solid data foundation for the subsequent risk assessment and implementation of intervention measures.

[0095] Example 4

[0096] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0097] S31. Based on the machine learning algorithm, a preliminary dehydration risk algorithm model is constructed in the cloud data processing platform, and the standard vital sign data of each patient in the cloud database is extracted through the API application program interface, and input into the preliminary dehydration risk algorithm model to calculate and output the preliminary dehydration risk index DS of each patient, and comprehensively quantify the dehydration index of each patient;

[0098] The preliminary dehydration risk indicator DS is calculated and output by the following preliminary dehydration risk algorithm model;

[0099]

[0100] Where, DS i represents the initial dehydration risk index of the i-th patient, ln represents the natural logarithm, UOsmi represents the urine osmotic pressure of the i-th patient, Pdi i represents the urination rate of the i-th patient, TBW i represents the total body water content of the i-th patient, HRV i Represents the heart rate variability index of the i-th patient, RHR i represents the heart rate response coefficient of the i-th patient, EL i represents the limb swelling of the i-th patient.

[0101] S32, by calculating the preliminary dehydration risk index DS of each patient, and based on the output result of the preliminary dehydration risk index DS, performing a preliminary assessment to screen patients with dehydration risk, the specific assessment content is as follows;

[0102] When the initial dehydration risk index DS of the i-th patient i When ≥0.05, it means that the patient is at risk of dehydration, and deep dehydration and edema analysis is performed at this time;

[0103] When the initial dehydration risk index DS of the i-th patient i When <0.05, it means the patient is normal and needs to be monitored continuously.

[0104] In this embodiment, the method first constructs a preliminary dehydration risk algorithm model in the cloud data processing platform, which combines the standard vital signs data of each patient. The standard vital signs data of each patient stored in the cloud database is extracted through the API interface, and input into the algorithm model for calculation, and finally the preliminary dehydration risk index DS of each patient is output. This indicator reflects the patient's dehydration risk level through the comprehensive quantification of multiple vital signs data. Then, based on the calculation results of the preliminary dehydration risk index DS, a preliminary assessment was performed on each patient. When the DS value is ≥0.05, it indicates that the patient may be at risk of dehydration and further deep dehydration and edema analysis is required; when the DS value is <0.05, it indicates that the patient is in a normal state and the system will continue to monitor. This screening method based on numerical thresholds can effectively distinguish which patients have potential dehydration risks and take targeted measures to intervene in time. Through this embodiment, the present invention realizes the automated assessment of postoperative dehydration risk based on big data and machine learning technology. Its beneficial effects are reflected in the following aspects: First, the use of machine learning algorithms can comprehensively consider the influence of multiple vital signs data, thereby improving the accuracy and scientificity of dehydration risk assessment; second, by introducing an automated assessment process, it reduces the bias of manual judgment and improves monitoring efficiency and processing speed; in addition, the system can automatically screen out high-risk patients based on preliminary assessment results, thereby providing strong decision-making support for subsequent interventions.

[0105] Example 5

[0106] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: S4 includes S41, S42 and S43;

[0107] S41. After preliminary screening of patients at risk of dehydration, further perform deep dehydration and edema analysis, which includes deep dehydration analysis and deep edema analysis;

[0108] The deep dehydration analysis uses nonlinear power function and exponential function, combined with urine osmotic pressure UOsm, total body water content TBW, urination rate Pdi, heart rate variability index HRV and limb swelling EL, to calculate and output the deep dehydration risk index DA for each screened patient;

[0109] The deep dehydration risk index DA is calculated and output by the following algorithm formula;

[0110]

[0111] In the formula, DA i represents the deep dehydration risk index of the i-th patient, exp represents the exponential function, and HRcrit represents the critical heart rate.

[0112] S42. Deep edema analysis calculates and outputs edema risk index EA by combining nonlinear power relationship, exponential function and standard vital sign data to analyze edema risk;

[0113] The edema risk index EA calculates the output through the following algorithm formula;

[0114]

[0115] Where Pfl represents fluid intake, EA i represents the edema risk index EA of the i-th patient.

[0116] S43, based on the acquired deep dehydration risk index DA and edema risk index EA, a comprehensive calculation is performed to obtain a comprehensive analysis index R, and the postoperative risk of the patient is analyzed;

[0117] The comprehensive analysis index R is calculated and output by the following algorithm formula;

[0118]

[0119] Where α and β represent the deep dehydration risk index DA of the i-th patient, respectively. i and the edema risk index EA for the i-th patient i The weight value of .

[0120] In this embodiment, the method performs a deep dehydration analysis on patients who are initially screened out as having a risk of dehydration. In this process, a nonlinear power function and exponential function model are used to calculate the deep dehydration risk index DA for each patient. This index not only takes into account the interaction of various physical sign data, but also introduces the critical heart rate HRcrit, which helps identify patients at high risk through deep analysis. At the same time, a deep edema analysis is further performed for patients at risk of dehydration. By combining nonlinear power relationships, exponential functions, and standard physical sign data such as fluid intake, the edema risk index EA for each patient is calculated. This analysis can effectively identify whether a patient has an edema risk and help medical staff understand changes in the patient's body fluid status. Next, the deep dehydration risk index DA and the edema risk index EA are comprehensively calculated to obtain a comprehensive analysis index R. This index weights and summarizes the risks of deep dehydration and edema through the weight value set by the user, and comprehensively evaluates the patient's overall postoperative risk. The introduction of the comprehensive analysis index R can integrate the assessment of dehydration and edema risks and provide a more comprehensive patient risk profile. The core advantages of this implementation are as follows: first, the joint analysis of deep dehydration and edema is more comprehensive, taking into account the multi-dimensional interaction of patients' postoperative signs and improving the accuracy of the assessment; second, through the combination of nonlinear and exponential models, it can handle complex clinical data relationships and significantly improve the sensitivity and predictive ability of the model; finally, the calculation of the comprehensive analysis index R provides clear and intuitive risk assessment results for subsequent patient management and intervention decisions.

[0121] Example 6

[0122] This embodiment is explained in Example 5. Please refer to Figure 1 , specifically: S5 includes S51 and S52;

[0123] S51. Based on the output results of the comprehensive analysis index R, a secondary comparative evaluation is conducted to analyze the dehydration and edema of the current postoperative urological patients, and intervention recommendations are generated based on the secondary comparative evaluation results. The specific evaluation contents are as follows;

[0124] When the comprehensive analysis index R>1, it means that the current patient is at risk of dehydration, and the first intervention recommendation is generated;

[0125] When the comprehensive analysis index R=1, it means that the patient's current risk of dehydration and edema is normal, and the patient's dehydration and edema are at critical values. At this time, there is no need for intervention and continuous testing;

[0126] When the comprehensive analysis index R < 1, it means that the current patient is at risk of edema, and the second intervention recommendation is generated;

[0127] S52, based on the intervention suggestions obtained, generating early warning information and pushing it to the client through the cloud data processing platform to prompt medical staff to intervene in the patient after urology surgery, and the client includes a mobile terminal and a nursing terminal;

[0128] The specific warning information is as follows:

[0129] When the first intervention suggestion is generated, the first warning message is pushed to remind medical staff that the patient is at risk of dehydration. At this time, the patient should increase the fluid supplement by 30%, adjust the drug dosage, and switch to intravenous fluid replacement.

[0130] When the second intervention suggestion is generated, a second warning message is pushed to remind medical staff that the patient is at risk of edema. At this time, medications such as diuretics are adjusted and patients are advised to exercise.

[0131] In this embodiment, the method analyzes the current patient's risk of dehydration and edema through a secondary comparative evaluation based on the comprehensive analysis index R, and then generates personalized intervention recommendations for each patient. Specifically, when the comprehensive analysis index R>1, it indicates that the patient has a high risk of dehydration. At this time, the first intervention recommendation is generated, prompting to increase fluid supplementation and adjust the drug dosage and treatment method according to the patient's condition; when R=1, it means that the patient's risk of dehydration and edema are both within the normal range, and no immediate intervention is required, only regular monitoring needs to be continued; when R<1, it indicates that the patient has an edema risk, and the second intervention recommendation is generated at this time, prompting medical staff to adjust diuretics and recommend patients to exercise appropriately. Then, according to the intervention recommendation, an early warning message is generated and pushed to the client through the cloud data processing platform. This mechanism informs medical staff of the patient's risk status and recommended intervention measures in a timely manner through real-time notifications on the mobile terminal and the nursing terminal. Specifically, when the first intervention recommendation is generated, the system will automatically push the early warning information to remind medical staff to increase fluid supplementation, adjust the drug regimen, and change to intravenous rehydration; while the second intervention recommendation prompts to adjust the use of diuretics and recommend patients to exercise to effectively manage edema problems. Through this implementation method, the system not only enhances the accuracy of risk assessment, but also ensures the timeliness and pertinence of intervention measures, and improves the accuracy of patient management. Its beneficial effects are reflected in the following aspects: First, the risk of dehydration and edema of patients is accurately identified and assessed, enabling medical staff to make scientific intervention decisions based on real-time data; second, by pushing personalized intervention suggestions, it ensures that the patient's care measures can be quickly implemented and avoids the risk of worsening of the disease; finally, the push mechanism of early warning information realizes real-time interaction between medical staff and patients, optimizes the postoperative management process, and improves the quality and efficiency of medical services.

[0132] Example 7

[0133] See also Figure 1and Figure 2 , a postoperative urology vital sign monitoring data processing system, including a postoperative vital sign collection module, a cloud processing module, a preliminary dehydration analysis module, a deep dehydration and edema analysis module and a comprehensive evaluation push module;

[0134] The postoperative vital signs collection module equips all patients after urological surgery with a sensor group to collect the postoperative vital signs data of all patients in real time, and builds a cloud data processing platform to transmit the collected postoperative vital signs data to the cloud data processing platform through the Internet of Things;

[0135] The cloud processing module pre-processes the postoperative vital sign data in the cloud data processing platform, obtains the standard vital sign data of each patient, and labels and numbers the standard vital sign data of each patient. At the same time, a cloud database is set up to store the standard vital sign data in the cloud database and sort them in the order of numbers;

[0136] The preliminary dehydration analysis module constructs a preliminary dehydration risk algorithm model in the cloud data processing platform, extracts the standard physical sign data of each patient and inputs them into the preliminary dehydration risk algorithm model, calculates and outputs the preliminary dehydration risk index DS of each patient, and conducts preliminary comparative evaluation based on the output results of the preliminary dehydration risk index DS to screen patients with dehydration risks;

[0137] The deep dehydration and edema analysis module screens out patients with dehydration risks through preliminary assessment and performs deep dehydration and edema analysis. The deep dehydration and edema analysis extracts standard physical sign data, calculates and outputs the deep dehydration risk index DA and edema risk index EA, and then comprehensively calculates the deep dehydration risk index DA and edema risk index EA to output the comprehensive risk index R;

[0138] The comprehensive assessment push module conducts a secondary comparative assessment based on the output results of the comprehensive risk index R, analyzes the current patient's dehydration and edema risks, generates warning information and sends it to the client to push warning notifications.

[0139] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for processing post-urology vital sign monitoring data, characterized in that: The following steps are involved: S1. Equip all patients after urological surgery with a sensor group to collect postoperative vital sign data of all patients in real time, build a cloud data processing platform, and transmit the collected postoperative vital sign data to the cloud data processing platform through the Internet of Things; S2. Preprocessing the postoperative vital sign data in the cloud data processing platform, obtaining standard vital sign data of each patient, marking and numbering the standard vital sign data of each patient, and setting up a cloud database, storing the standard vital sign data in the cloud database, and sorting them in order of numbers; S3. Construct a preliminary dehydration risk algorithm model in the cloud data processing platform, extract the standard physical sign data of each patient and input them into the preliminary dehydration risk algorithm model respectively, calculate and output the preliminary dehydration risk index DS of each patient, perform preliminary comparative evaluation based on the output results of the preliminary dehydration risk index DS, and screen patients with dehydration risk; S4. For patients who are screened out as having a risk of dehydration through preliminary assessment, perform a deep dehydration and edema analysis, wherein the deep dehydration and edema analysis extracts standard physical sign data, calculates and outputs a deep dehydration risk index DA and an edema risk index EA, and then comprehensively calculates the deep dehydration risk index DA and the edema risk index EA to output a comprehensive risk index R; S5. Perform a secondary comparative evaluation based on the output results of the comprehensive risk index R, analyze the current patient's dehydration and edema risks, generate warning information and send it to the client to push warning notifications.

2. The method for processing post-urology vital sign monitoring data according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, equipping all patients after urology surgery with a sensor group, and setting the acquisition frame rate of the sensor group to once per minute, so as to collect postoperative vital sign data of all patients after urology surgery in real time; The sensor group includes a urine analyzer, a smart weight scale, a blood oxygen saturation detector, an electrocardiogram device, a limb dimension measuring instrument, and a bioelectrical impedance analyzer; The postoperative physical sign data include urination rate Pdi, urine osmotic pressure UOsm, heart rate variability index HRV, heart rate response coefficient RHR, total body water content TBW and limb swelling EL; S12. Through the communication module in the sensor group, use the MQTT protocol of the Internet of Things to encapsulate the postoperative vital sign data, obtain the vital sign data packet, and generate a unique identifier for each vital sign data packet, and then transmit the vital sign data packet to the cloud data processing platform through the wireless communication network.

3. The method for processing post-urology vital sign monitoring data according to claim 2, characterized in that: The S2 includes S21 and S22; S21. Receive the vital sign data packet in real time in the cloud data processing platform, unseal the vital sign data packet, extract the postoperative vital sign data, and set an API application program interface to integrate the cloud data processing platform with the case system, match the patient case information in the case library based on the unique identifier, and extract the patient's initials and the last four digits of the ID card; After matching the patient case information, preprocessing is performed on the postoperative vital sign data of all patients, and the preprocessing includes abnormal data elimination and data standardization, obtaining standard vital sign data, and labeling the standard vital sign data of each patient with a number; The number is composed in the format of: initials of name / last four digits of ID card / year, month, day / last two digits of unique identifier; The abnormal data elimination is carried out by using the data anomaly detection algorithm z-score on the postoperative vital sign data to eliminate sensor failure and data deviation; The data standardization is performed by denoising, filling missing values ​​and normalizing the postoperative vital sign data; S22. At the same time, a cloud database is set up, and an API application program interface is set up to integrate the cloud data processing platform with the cloud database, store the standard vital signs data in the cloud database, and sort them in order of number.

4. The method for processing post-urology vital sign monitoring data according to claim 3, characterized in that: The S3 includes S31 and S32; S31. Based on the machine learning algorithm, a preliminary dehydration risk algorithm model is constructed in the cloud data processing platform, and the standard vital sign data of each patient in the cloud database is extracted through the API application program interface, and input into the preliminary dehydration risk algorithm model to calculate and output the preliminary dehydration risk index DS of each patient, and comprehensively quantify the dehydration index of each patient; The preliminary dehydration risk indicator DS is calculated and output by the following preliminary dehydration risk algorithm model; Where, DS i represents the initial dehydration risk index of the i-th patient, ln represents the natural logarithm, UOsm i represents the urine osmotic pressure of the i-th patient, Pdi i represents the urination rate of the i-th patient, TBW i represents the total body water content of the i-th patient, HRV i Represents the heart rate variability index of the i-th patient, RHR i represents the heart rate response coefficient of the i-th patient, EL i represents the limb swelling of the i-th patient.

5. A method for processing post-urology vital sign monitoring data according to claim 4, characterized in that: S32, by calculating the preliminary dehydration risk index DS of each patient, and based on the output result of the preliminary dehydration risk index DS, performing a preliminary assessment to screen patients with dehydration risk, the specific assessment content is as follows; When the initial dehydration risk index DS of the i-th patient i When ≥0.05, it means that the patient is at risk of dehydration, and deep dehydration and edema analysis is performed at this time; When the initial dehydration risk index DS of the i-th patient i When <0.05, it means the patient is normal and needs to be monitored continuously.

6. The method for processing post-urology vital sign monitoring data according to claim 1, characterized in that: The S4 includes S41, S42 and S43; S41, after preliminary screening of patients at risk of dehydration, further performing deep dehydration and edema analysis, wherein the deep dehydration and edema analysis includes deep dehydration analysis and deep edema analysis; The deep dehydration analysis uses a nonlinear power function and an exponential function, combined with urine osmotic pressure UOsm, total body water content TBW, urination rate Pdi, heart rate variability index HRV and limb swelling EL, to calculate and output a deep dehydration risk index DA for each screened patient; The deep dehydration risk index DA is calculated and output by the following algorithm formula: In the formula, DA i represents the deep dehydration risk index of the i-th patient, exp represents the exponential function, and HRcrit represents the critical heart rate.

7. A method for processing post-urology vital sign monitoring data according to claim 6, characterized in that: S42, the depth edema analysis is performed by combining a nonlinear power relationship, an exponential function and standard physical sign data to calculate and output an edema risk index EA to analyze the edema risk; The edema risk index EA is calculated and output by the following algorithm formula; Where Pfl represents fluid intake, EA i represents the edema risk index EA of the i-th patient.

8. The method for processing post-urology vital sign monitoring data according to claim 6, characterized in that: S43, based on the acquired deep dehydration risk index DA and edema risk index EA, a comprehensive calculation is performed to obtain a comprehensive analysis index R, and the postoperative risk of the patient is analyzed; The comprehensive analysis index R is calculated and output by the following algorithm formula; Where α and β represent the deep dehydration risk index DA of the i-th patient, respectively. i and the edema risk indicator EA for the i-th patient i The weight value is α+β=1, and the specific value is set by the user.

9. A method for processing post-urology vital sign monitoring data according to claim 8, characterized in that: The S5 includes S51 and S52; S51. Based on the output results of the comprehensive analysis index R, a secondary comparative evaluation is conducted to analyze the dehydration and edema of the current postoperative urological patients, and intervention recommendations are generated based on the secondary comparative evaluation results. The specific evaluation contents are as follows; When the comprehensive analysis index R>1, it means that the current patient is at risk of dehydration, and the first intervention recommendation is generated; When the comprehensive analysis index R=1, it means that the patient's current risk of dehydration and edema is normal, and there is no need for intervention and continuous testing; When the comprehensive analysis index R < 1, it means that the current patient is at risk of edema, and the second intervention recommendation is generated; S52, based on the obtained intervention suggestions, generating early warning information and pushing it to the client through the cloud data processing platform to prompt medical staff to intervene in the patient after urology surgery, wherein the client includes a mobile terminal and a nursing terminal; The specific warning information is as follows: When the first intervention suggestion is generated, the first warning message is pushed to remind medical staff that the patient is at risk of dehydration. At this time, the patient should increase the fluid supplement by 30%, adjust the drug dosage, and switch to intravenous fluid replacement. When the second intervention suggestion is generated, a second warning message is pushed to remind medical staff that the patient is at risk of edema. At this time, the medication is adjusted and the patient is advised to exercise.

10. A system for processing data of post-urology vital sign monitoring, applied to a method for processing data of post-urology vital sign monitoring according to any one of claims 1 to 9, characterized in that: It includes postoperative vital signs collection module, cloud processing module, preliminary dehydration analysis module, deep dehydration and edema analysis module and comprehensive evaluation push module; The postoperative vital sign collection module collects the postoperative vital sign data of all patients in real time by equipping all patients after urological surgery with a sensor group, and builds a cloud data processing platform to transmit the collected postoperative vital sign data to the cloud data processing platform through the Internet of Things; The cloud processing module obtains the standard vital sign data of each patient by preprocessing the postoperative vital sign data in the cloud data processing platform, and marks and numbers the standard vital sign data of each patient, and sets a cloud database, stores the standard vital sign data in the cloud database, and sorts them in the order of numbers; The preliminary dehydration analysis module constructs a preliminary dehydration risk algorithm model in the cloud data processing platform, extracts the standard physical sign data of each patient and inputs them into the preliminary dehydration risk algorithm model respectively, calculates and outputs a preliminary dehydration risk index DS for each patient, and performs preliminary comparative evaluation based on the output results of the preliminary dehydration risk index DS to screen patients with dehydration risks; The deep dehydration and edema analysis module screens out patients with dehydration risks through preliminary assessment, and performs deep dehydration and edema analysis. The deep dehydration and edema analysis extracts standard physical sign data, calculates and outputs a deep dehydration risk index DA and an edema risk index EA, and then comprehensively calculates the deep dehydration risk index DA and the edema risk index EA to output a comprehensive risk index R; The comprehensive assessment push module performs a secondary comparative assessment based on the output results of the comprehensive risk index R, analyzes the dehydration and edema risks of the current patient, generates warning information and sends it to the client to push warning notifications.

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