Intelligent data processing method and system based on surgical nursing

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent data processing method and system based on surgical nursing, and the method comprises the steps: firstly obtaining food intake and physiological indexes of a patient within a preset time, comparing the food intake and physiological indexes with a standard food intake set, and generating a food intake standard score; in conjunction with the physiological metric, the system determines a real-time feeding score and a total feeding score. By comparing the real-time and total feeding scores at different time points, the system finally generates a feeding balance score of the patient. According to the method, food intake and physiological data are effectively integrated, a scientific and quantitative food intake evaluation tool is provided for surgical nursing, nutrition management of a patient is optimized, nursing quality is improved, the absolute value of the food intake is considered in the evaluation, the relevance between time distribution of food intake and physiological indexes is concerned, and the evaluation accuracy is improved. Therefore, more comprehensive and in-depth patient state information is provided for nursing personnel. Especially, due to the introduction of a feeding balance degree score, an important basis is provided for formulating a more reasonable nutrition intervention scheme.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to an intelligent data processing method and system based on surgical nursing. Background Art

[0002] With the continuous advancement of medical technology, the quality of postoperative care has become one of the key factors affecting the patient's recovery speed and prognosis. In traditional surgical nursing practice, the patient's eating situation and physiological index monitoring mainly rely on the manual records and experience judgment of the nurses. Although this method is intuitive and simple, it has many problems. First, manual records are prone to errors and omissions, especially when the nurses are busy, the accuracy and completeness of the records are difficult to guarantee. Secondly, the monitoring of physiological indicators is often intermittent and cannot capture the dynamic changes of the patient's status in time. In addition, traditional methods are difficult to achieve accurate quantification and comprehensive evaluation of the patient's eating situation, and can often only roughly estimate the amount of food intake, while ignoring important factors such as the distribution of eating time and the correlation with physiological indicators.

[0003] In recent years, with the development of information technology, some medical institutions have begun to try to introduce electronic nursing record systems. Although such systems have improved the convenience of records and the readability of data to a certain extent, they still have obvious limitations. Most systems simply digitize paper records and lack intelligent analysis and decision support functions. They are unable to conduct in-depth analysis of patients' eating data and physiological indicators, nor can they provide timely and effective intervention suggestions for nursing staff. At the same time, these systems usually operate independently and are difficult to achieve seamless connection with other information systems in the hospital, resulting in the formation of data islands and hindering the efficient circulation and comprehensive utilization of information.

[0004] More importantly, existing nursing methods and systems generally lack a comprehensive assessment mechanism for patients' eating conditions. They often only focus on whether the amount of food eaten at a single time meets the standard, while ignoring the balance and continuity of eating. This one-sided assessment method cannot accurately reflect the patient's actual nutritional intake, which may lead to delayed or inappropriate nutritional intervention measures. In addition, existing methods also make it difficult to effectively link patients' eating conditions with changes in their physiological indicators, thus missing the opportunity to detect potential problems early.

[0005] In the face of these problems, a new method that can comprehensively, accurately and intelligently process surgical nursing data is urgently needed. This method should be able to continuously monitor and evaluate the patient's eating status in multiple dimensions, organically combine eating data with physiological indicators, and provide timely and personalized nursing decision support. At the same time, it should also have good scalability and compatibility, and be able to seamlessly connect with the hospital's existing information system to achieve efficient sharing and utilization of data. Summary of the invention

[0006] The present invention is proposed to address the above-mentioned problems. It aims to construct an intelligent data processing method and system based on surgical nursing, and comprehensively improve the accuracy and efficiency of surgical nursing through advanced data collection, analysis and evaluation methods. The core goal of the present invention is to solve a series of problems in traditional nursing methods, such as inaccurate data collection, incomplete evaluation, in-depth analysis, and untimely decision support, and to provide strong technical support for the realization of precise and personalized surgical nursing.

[0007] The present invention provides an intelligent data processing method based on surgical nursing, comprising: The acquisition steps include: Obtaining the patient's first food intake and first physiological index within a predetermined time; Get the set of standard food intake corresponding to a preset single meal; Processing steps include: generating a plurality of first eating standard scores based on the first food intake amount and the standard food intake amount set; Determining a first real-time eating score according to the first physiological indicator and the first eating standard score; generating a first total eating score based on the first food intake amount and the first physiological indicator; Obtaining a second food intake and a second physiological index of the patient within a predetermined time; generating a plurality of second eating standard scores based on the second eating amount and the standard eating amount set; Determining a second real-time eating score according to the second physiological indicator and the second eating standard score; generating a second total eating score based on the second food intake amount and the second physiological indicator; Output steps include: A patient's eating balance score within a predetermined time is generated according to the first real-time eating score number, the first total eating score, the second real-time eating score number and the second total eating score.

[0008] Preferably, the processing step further comprises: Based on the first food intake amount, dividing the standard food intake amount set into N first food intake amounts and corresponding n food intake standard scores; Determine a set of eating standards corresponding to the first eating amounts according to the N first eating amounts; Based on the eating standard set corresponding to the first eating amount, gain processing is performed on each first eating standard score to obtain a second eating standard score.

[0009] Preferably, the processing step further comprises: Based on each of the second eating standard scores, obtaining n first physiological indicators corresponding to the N first eating amounts; Determine, according to the n first physiological indicators corresponding to each of the first food intake amounts, an average value of the n first physiological indicators of each of the first food intake amounts; Generate n first real-time eating standard scores based on each first physiological indicator mean value and the second eating standard score; Determining a first real-time eating score set for a first eating amount set according to the n first real-time eating standard scores; Based on the first real-time eating score set, a first real-time eating score number of the patient within a predetermined time is determined.

[0010] Preferably, the processing step further comprises: Based on each standard food intake amount in the standard food intake amount set, obtaining a corresponding food intake reference threshold; According to the reference eating threshold, setting an eating threshold corresponding to each standard eating amount based on the first eating amount; Determining an eating threshold corresponding to each first eating amount in the first eating amount set based on a matching result between the first eating amount and the standard eating amount set; Obtaining an eating score for each first food intake amount according to the first eating standard score, the eating threshold, and the first real-time eating score number corresponding to each first food intake amount; generating a total eating score of the first food intake set based on the eating score of each of the first food intake amounts; A first total eating score of the patient within a predetermined time is determined according to the total eating score of the first eating amount set.

[0011] Preferably, the total eating score of the first eating amount set is generated by the formula: , in, is the total eating score of the first eating amount set, is the food intake coefficient, is the number of first food intake in the first food intake set, is the first eating standard score corresponding to the qth first eating amount, is the eating threshold corresponding to the qth first eating amount, is the first real-time eating score corresponding to the qth first eating amount, is the reference value of the first eating standard score corresponding to the qth first eating amount.

[0012] Preferably, the processing step further comprises: Based on each of the second eating standard scores, obtaining n second physiological indicators corresponding to each second eating amount in the second eating amount set; Determine, according to the n second physiological indicators corresponding to each of the second food intake amounts, an average value of the n second physiological indicators of each of the second food intake amounts; Generate n second real-time eating standard scores based on each second physiological indicator mean value and the second eating standard score; Determining a second real-time eating score set for a second eating amount according to the n second real-time eating standard scores; Based on the second real-time eating score set, a second real-time eating score number of the patient within a predetermined time is determined.

[0013] Preferably, the processing step further comprises: Based on each standard food intake amount in the standard food intake amount set, obtaining a corresponding food intake reference threshold; According to the reference eating threshold, setting an eating threshold corresponding to each standard eating amount based on the second eating amount; Based on the matching result between the second food intake amount and the standard food intake amount set, determining the food intake threshold value corresponding to each second food intake amount in the second food intake amount set; Obtaining an eating score for each second eating amount according to the second eating standard score, the eating threshold, and the second real-time eating score number corresponding to each second eating amount; generating a total eating score of the second food intake amount based on the eating score of each of the second food intake amounts; A second total eating score of the patient within a predetermined time is determined according to the total eating score of the second food intake amount.

[0014] Preferably, the formula for generating the total eating score of the second eating amount is: , in, is the total eating score of the second eating amount, is the food intake coefficient, is the number of second food intake in the second food intake set, is the second eating standard score corresponding to the r-th second eating amount, is the feeding threshold corresponding to the rth second feeding amount, is the second real-time eating score corresponding to the r-th second eating amount, is the reference value of the second eating standard score corresponding to the r-th second eating amount.

[0015] Preferably, in the output step, the formula for generating the patient's eating balance score within a predetermined time is: , in, Score the patient's eating balance within a predetermined time. is the number of first difference values ​​in the first difference set, is the number of second difference values ​​in the second difference value set, is the absolute value of the i-th first difference, is the minimum absolute value of all first difference values ​​in the first difference set, is the preset maximum value. For the The absolute value of the second difference, is the minimum value of the absolute values ​​of all second difference values ​​in the second difference value set, is the preset maximum value.

[0016] An intelligent data processing system based on surgical care for executing the method, comprising: Get modules for: Obtaining the patient's first food intake and first physiological index within a predetermined time; Get the set of standard food intake corresponding to a preset single meal; Processing modules for: generating a plurality of first eating standard scores based on the first food intake amount and the standard food intake amount set; Determining a first real-time eating score according to the first physiological indicator and the first eating standard score; generating a first total eating score based on the first food intake amount and the first physiological indicator; Obtaining a second food intake and a second physiological index of the patient within a predetermined time; generating a plurality of second eating standard scores based on the second eating amount and the standard eating amount set; Determining a second real-time eating score according to the second physiological indicator and the second eating standard score; generating a second total eating score based on the second food intake amount and the second physiological indicator; Output modules for: A patient's eating balance score within a predetermined time is generated according to the first real-time eating score number, the first total eating score, the second real-time eating score number and the second total eating score.

[0017] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0018] The present invention realizes a comprehensive and accurate assessment of the patient's eating status by innovatively introducing a multi-dimensional eating scoring system, a real-time dynamic evaluation mechanism, and an eating balance scoring method. This assessment not only takes into account the absolute value of the amount of food intake, but also focuses on the time distribution of eating and its correlation with physiological indicators, thereby providing nursing staff with more comprehensive and in-depth patient status information. In particular, the introduction of the eating balance scoring fills the gap in the existing technology in assessing the long-term eating status of patients, and provides an important basis for formulating a more reasonable nutritional intervention plan.

[0019] In addition, the intelligent data processing system of the present invention greatly reduces human errors and improves the accuracy and completeness of data through automated data collection and analysis. The real-time monitoring and alarm functions of the system can detect potential abnormalities in a timely manner, allowing nursing staff to respond quickly and effectively reducing the risk of complications. At the same time, the intelligent analysis function of the system provides decision support for nursing staff, helping them to better formulate personalized nursing plans, and improving the pertinence and effectiveness of nursing.

[0020] The method and system of the present invention show significant advantages in practical applications. It not only greatly improves the efficiency of nursing work and reduces the workload of nursing staff, but also improves the prognosis of patients through precise nutritional management. The ease of use and visual data display function of the system also improve the patient's participation and satisfaction, and promote doctor-patient communication. In the long run, the present invention helps to optimize the allocation of medical resources, reduce medical costs, and make important contributions to promoting the development of smart medicine.

[0021] In summary, the intelligent data processing method and system based on surgical nursing provided by the present invention effectively solves many problems existing in traditional surgical nursing through multi-dimensional data collection, intelligent analysis and accurate evaluation. It not only improves the efficiency and quality of nursing work, but also opens up a new way to achieve precise and personalized surgical nursing, which has important theoretical value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is a flow chart of the method of the present invention.

[0023] Figure 2 Flowchart for the generation of the eating criteria score of the present invention.

[0024] Figure 3 The present invention is a flow chart for generating a real-time eating score.

[0025] Figure 4 The flowchart of the total eating score generation of the present invention is shown in FIG.

[0026] Figure 5The present invention is a flow chart for generating a balanced eating score.

[0027] Figure 6 It is the overall logic block diagram of the system of the present invention. DETAILED DESCRIPTION

[0028] Please refer to Figure 1-6 The present invention discloses an intelligent data processing method and system based on surgical nursing. The method aims to improve the accuracy and efficiency of surgical nursing through intelligent data processing. The specific implementation of the present invention will be described in detail below.

[0029] The method of the present invention includes an acquisition step, a processing step and an output step. First, in the acquisition step, the method acquires the patient's first food intake and first physiological index within a predetermined time, as well as a preset set of standard food intake corresponding to a single meal. The predetermined time here can be 24 hours, or a shorter or longer time period, which can be flexibly set according to clinical needs. The first food intake can be the actual food intake of the patient during the time period, recorded in grams (g). The first physiological index may include physiological parameters related to eating, such as blood sugar level, blood pressure, and body temperature.

[0030] Preferably, in one embodiment of the present invention, the standard food intake set can be pre-set based on factors such as the patient's age, gender, weight, disease type, etc. For example, for an adult male patient weighing 70 kg, the standard food intake set may include 300-400 g for breakfast, 450-550 g for lunch, 400-500 g for dinner, etc.

[0031] like Figure 3 As shown, next, in the processing step, the method first generates a plurality of first eating standard scores based on the first eating amount and the standard eating amount set. This process can be implemented by the following formula: , in, Score the i-th first eating standard, For the first food intake, is the i-th standard food intake in the standard food intake set, is the scoring function. The scoring function can be a nonlinear function, for example: , The design concept of this scoring function is that when the actual food intake is within the range of 90% to 110% of the standard food intake, a full score of 100 points is given; when the deviation from the standard food intake is small, points are linearly deducted according to the degree of deviation; when the deviation is too large, 0 points are given. This design can encourage patients to maintain a moderate food intake.

[0032] Then, the method determines the first real-time eating score number according to the first physiological indicator and the first eating standard score. This step can be achieved by the following formula: , in, is the first real-time eating score number, For the The weight of the first eating criterion score, The number of scores for the first eating criterion, As the first physiological indicator, Adjust functions for physiological indicators.

[0033] The physiological indicator adjustment function can be designed according to the specific physiological indicator type. For example, for blood sugar level, the following function can be designed: , This function is designed to take into account the normal blood sugar range (4.4-7.0 mmol / L) as well as mild hyperglycemia and hypoglycemia.

[0034] Next, please refer to Figure 4 , the method generates a first total eating score based on the first food intake amount and the first physiological indicator. This can be achieved by the following formula: , in, is the first total eating score, is the weight coefficient is a comprehensive evaluation function. The comprehensive evaluation function can be designed based on clinical experience, for example: , in, For the ideal food intake, It is the ideal physiological index value.

[0035] In another embodiment of the present invention, the processing step further includes obtaining a second food intake amount and a second physiological index of the patient within a predetermined time, and repeating the above process to generate multiple second food intake standard scores, determine a second real-time food intake score number, and generate a second total food intake score. This design allows the system to evaluate the patient's food intake multiple times, thereby more comprehensively understanding the patient's food intake status.

[0036] Finally, please refer to Figure 5 In the output step, the method generates a patient's eating balance score within a predetermined time according to the first real-time eating score number, the first total eating score, the second real-time eating score number, and the second total eating score. The eating balance score can be calculated by the following formula: , in, Score the balance of eating. is the second real-time eating score number, is the second overall eating score, is the maximum possible difference in the score. The design concept of this formula is that when the real-time eating score and the total eating score are closer, the eating balance is higher.

[0037] An important feature of the method of the present invention is that it not only takes into account the amount of food intake, but also the changes in physiological indicators. This comprehensive assessment can more accurately reflect the patient's actual eating status and physical response. For example, even if the patient's food intake meets the standard, if the physiological indicators are abnormal, the overall score will be lowered, thereby alerting medical staff to potential problems.

[0038] In addition, this method can evaluate the balance of patients' eating by comparing eating conditions at different time periods. This is especially important for some patients who need to strictly control their diet (such as diabetic patients). Medical staff can adjust the patient's diet plan or take other intervention measures in a timely manner based on the eating balance score.

[0039] In practical applications, this method can be integrated with the hospital's information system to automatically obtain the patient's eating records and physiological index data. The scoring results can be directly displayed on the interface of the nurse workstation, allowing medical staff to quickly understand the patient's eating status. For patients with low scores or unbalanced eating, the system can automatically generate an alarm to remind medical staff to intervene.

[0040] In general, the intelligent data processing method provided by the present invention can significantly improve the efficiency and quality of surgical care. It not only reduces the workload of medical staff, but also can promptly detect potential nutritional problems and provide strong support for the patient's recovery. In a preferred embodiment of the present invention, the processing step further refines the generation process of the first eating standard score. Specifically, the method first divides the standard eating amount set into N first eating amounts and corresponding n eating amount standard scores based on the first eating amount. This division can more finely reflect the difference between the patient's eating situation and the standard.

[0041] For example, suppose the standard food intake set includes three standard values: breakfast 300g, lunch 500g, and dinner 400g, and the patient's first food intake is 280g. Then, the system may divide it into: breakfast 280g (corresponding to the standard 300g), lunch 0g (corresponding to the standard 500g), dinner 0g (corresponding to the standard 400g). Such a division can clearly show which eating period the patient has a difference from the standard.

[0042] Next, the method determines the set of eating standards corresponding to the first eating amounts based on the N first eating amounts. The purpose of this step is to find the closest standard eating amount for each actual eating amount. Continuing with the above example, the actual eating amount of 280g will correspond to the standard eating amount of 300g.

[0043] After determining the corresponding relationship, this method performs gain processing on each first eating standard score based on the eating standard set corresponding to the first eating amount to obtain the second eating standard score. The purpose of gain processing is to adjust the score to better reflect the actual situation. Gain processing can be achieved by the following formula: , in, Score the second eating standard for the i-th time, Score the i-th first eating standard, is the first food intake of the ith is the corresponding standard food intake, is the gain coefficient (usually a value between 0.1 and 0.5). The design concept of this formula is that when the actual food intake is different from the standard food intake, a greater score adjustment will be given.

[0044] Another embodiment of the present invention further refines the process of determining the first real-time eating score. First, based on each second eating standard score, n first physiological indicators corresponding to N first eating amounts are obtained. The n first physiological indicators here may include blood sugar levels before and after eating, blood pressure changes, etc.

[0045] Then, according to the n first physiological indicators corresponding to each first food intake, the mean of the n first physiological indicators of each first food intake is determined. The purpose of this step is to eliminate the error that may be caused by a single measurement and improve the reliability of the data. The mean of the physiological indicator can be calculated by a simple arithmetic average: , in, is the mean value of the first physiological indicator of the i-th first food intake, is the jth first physiological indicator corresponding to the i-th first food intake.

[0046] Next, the method generates n first real-time eating standard scores based on each first physiological indicator mean and the second eating standard score. This can be achieved by the following formula: , in, is the first real-time eating standard score of the i-th, Adjust the functions for the physiological indicators mentioned above.

[0047] Finally, according to the n first real-time eating standard scores, the first real-time eating score set of the first eating amount set is determined, and based on this, the number of first real-time eating scores of the patient within the predetermined time is determined. This can be achieved by weighted average: , in, The weight of the i-th first real-time eating standard score may be set according to the size or importance of the eating amount.

[0048] In another preferred embodiment of the present invention, the processing step also includes a detailed calculation process of the first total eating score. First, based on each standard eating amount in the standard eating amount set, the corresponding eating reference threshold is obtained. The eating reference threshold can be set according to clinical experience. For example, 80% to 120% of the standard eating amount can be set as the reference threshold range.

[0049] Then, according to the reference threshold value, set the corresponding eating threshold value for each standard eating amount based on the first eating amount. The purpose of this step is to determine an acceptable range for each actual eating amount. For example, if the standard breakfast eating amount is 300g and the reference threshold range is 80% to 120%, then the corresponding eating threshold may be 240g-360g.

[0050] Next, based on the matching result of the first food intake amount and the standard food intake amount set, the food intake threshold corresponding to each first food intake amount in the first food intake amount set is determined. This step matches the actual food intake amount with the standard food intake amount and assigns a corresponding threshold to each actual food intake amount.

[0051] Afterwards, the eating score of each first eating amount is obtained according to the first eating standard score, the eating threshold and the first real-time eating score number corresponding to each first eating amount. This can be achieved by the following formula: , in, is the eating score of the i-th first eating amount, is the weight coefficient (0<β<1), Score the i-th first eating standard, is the first real-time eating score of the i-th is the eating threshold corresponding to the i-th first eating amount.

[0052] Finally, based on the eating score of each first eating amount, a total eating score of the first eating amount set is generated, and the first total eating score of the patient within a predetermined time is determined based on the total eating score. This process can be achieved by the formula mentioned above: , in, is the total eating score of the first eating amount set, is the food intake coefficient (usually between 0.3 and 0.7), is the number of first food intake in the first food intake set, is the first eating standard score corresponding to the qth first eating amount, is the eating threshold corresponding to the qth first eating amount, is the first real-time eating score corresponding to the qth first eating amount, is the reference value of the first eating standard score corresponding to the qth first eating amount.

[0053] This complex scoring calculation method takes into account multiple factors, including the difference between the actual food intake and the standard, changes in physiological indicators, and the time distribution of eating. Through this comprehensive evaluation, the method of the present invention can more accurately reflect the patient's eating status and provide more valuable information for medical staff.

[0054] In actual applications, these parameters and thresholds can be adjusted according to specific clinical needs. For example, for patients who need to strictly control their diet (such as diabetic patients), a narrower eating threshold range can be set; for patients in the recovery period, the standards can be appropriately relaxed. This flexibility allows the method of the present invention to adapt to different medical scenarios and patient needs. In another embodiment of the present invention, the processing step also includes a detailed processing process for the second food intake. This process is similar to the processing process for the first food intake, but is aimed at another eating period of the patient within a predetermined time.

[0055] First, based on each second eating standard score, the method obtains n second physiological indicators corresponding to each second eating amount in the second eating amount set. These physiological indicators may include blood sugar level, blood pressure change, insulin level, etc. after eating. For example, for a diabetic patient, the system may record the blood sugar level 1 hour, 2 hours and 3 hours after lunch as the second physiological indicator.

[0056] Next, according to the n second physiological indicators corresponding to each second food intake, the mean of the n second physiological indicators of each second food intake is determined. The purpose of this step is to eliminate the fluctuations that may be caused by a single measurement and obtain more stable physiological indicator data. The calculation method of the mean of the physiological indicator can be weighted average: , in, is the mean value of the second physiological index of the second food intake of the ith person, is the second smallest physiological index corresponding to the i-th second food intake, is the weight of the jth physiological indicator. The weight can be set according to the importance of the indicator or the order of measurement time. Then, this method generates n second real-time eating standard scores based on the mean of each second physiological indicator and the second eating standard score. This process can be achieved by the following formula: , in, Score the i-th second real-time eating standard, Score the second eating standard for the i-th time, Adjusting the function for physiological indicators This function can be designed according to the characteristics of different physiological indicators, for example: , in, is the ideal physiological index value. The design concept of this function is that when the actual physiological index is close to the ideal value, the function value is close to 1; when it deviates from the ideal value, the function value decreases. Finally, according to the n second real-time eating standard scores, the second real-time eating score set of the second eating amount is determined, and based on this, the number of second real-time eating scores of the patient within the predetermined time is determined. This can be achieved by weighted summation: , in, Second real-time eating score The weight of the i-th second real-time eating standard score. The weight can be set according to the importance of each eating period. For example, for some patients, the weight of dinner may be higher than that of breakfast.

[0057] In another preferred embodiment of the present invention, the processing step further includes a detailed calculation process of the second total eating score, which is similar to the calculation of the first total eating score, but is for the second eating amount.

[0058] First, based on each standard food intake in the standard food intake set, the corresponding food intake reference threshold is obtained. These thresholds can be set according to the patient's specific situation and medical needs. For example, for a patient who needs to control the dinner intake, the dinner intake reference threshold may be set more strictly than breakfast and lunch.

[0059] Then, according to the reference threshold for eating, set the eating threshold corresponding to each standard eating amount based on the second eating amount. The purpose of this step is to determine an acceptable range for each actual eating amount. For example, if the standard dinner eating amount is 400g and the reference threshold range is 90% to 110%, then the corresponding eating threshold may be 360g-440g.

[0060] Next, based on the matching result of the second food intake amount and the standard food intake amount set, the food intake threshold corresponding to each second food intake amount in the second food intake amount set is determined. This step matches the actual food intake amount with the standard food intake amount and assigns a corresponding threshold to each actual food intake amount.

[0061] Afterwards, the eating score of each second eating amount is obtained according to the second eating standard score, the eating threshold and the second real-time eating score number corresponding to each second eating amount. This can be achieved by the following formula: , in, is the eating score of the i-th second eating amount, is the weight coefficient , Score the second eating standard for the i-th time, is the second real-time eating score of the ith time, is the eating threshold corresponding to the i-th second eating amount.

[0062] Finally, a total eating score of the second eating amount is generated based on the eating score of each second eating amount, and a second total eating score of the patient within a predetermined time is determined based on the total eating score. This process can be implemented by the formula mentioned above: , in, is the total eating score of the second eating amount, is the food intake coefficient (usually between 0.3 and 0.7), is the number of second food intake in the second food intake set, is the second eating standard score corresponding to the r-th second eating amount, is the feeding threshold corresponding to the rth second feeding amount, is the second real-time eating score corresponding to the r-th second eating amount, is the reference value of the second eating standard score corresponding to the r-th second eating amount.

[0063] In the last embodiment of the present invention, the output step describes in detail the process of generating a score of the patient's eating balance during the predetermined time. The purpose of this score is to evaluate whether the patient's eating is balanced during the entire predetermined time.

[0064] The formula for generating the eating balance score is as follows: , In this formula, Score the patient's eating balance within a predetermined time. is the number of first difference values ​​in the first difference set, is the number of second difference values ​​in the second difference value set. and represent the difference values ​​in the first and second difference value sets respectively, which reflect the difference between the real-time eating score and the total eating score. and are the minimum absolute values ​​of all differences in the first and second difference sets, respectively, and and This is the preset maximum value.

[0065] The design concept of this formula is that when the real-time eating score is closer to the total eating score, the difference is smaller, and thus the eating balance score is higher. The highest eating balance score is 1, indicating a completely balanced eating state.

[0066] In practical applications, medical staff can use this eating balance score to assess the patient's overall eating status. For example, if a patient's eating balance score is lower than 0.7, it may mean that the patient's eating time is unevenly distributed, or the amount of food consumed during certain eating periods is significantly different from the ideal state. At this time, medical staff may need to adjust the patient's eating plan or further understand the cause of the imbalance.

[0067] In general, the intelligent data processing method based on surgical care provided by the present invention can provide medical staff with a more comprehensive and accurate assessment of the patient's eating status by comprehensively considering multiple factors such as the patient's food intake, changes in physiological indicators, and distribution of eating time. This can not only help medical staff better monitor the patient's recovery, but also provide an important basis for formulating personalized nutrition plans, thereby improving the quality and efficiency of surgical care.

[0068] The present invention also provides an intelligent data processing system based on surgical nursing, which is a hardware carrier for implementing the above method. Preferably, the system includes an acquisition module 1, a processing module 2 and an output module 3, which are interconnected through a data bus and work together to complete the intelligent data processing task.

[0069] The acquisition module 1 is mainly responsible for the initial data collection. Specifically, the acquisition module 1 is used to obtain the patient's first food intake and first physiological index within a predetermined time, and to obtain a preset standard food intake set corresponding to a single meal. In actual applications, the acquisition module 1 may include multiple sensor submodules, such as a food weight sensor, a blood glucose monitor, etc. These submodules can collect the patient's food intake data and physiological indicators in real time. In addition, the acquisition module 1 may also be connected to the hospital's electronic medical record system to obtain preset standard food intake set information.

[0070] Processing module 2 is the core of this system and is responsible for complex processing and analysis of the acquired data. Processing module 2 first generates multiple first eating standard scores based on the first food intake amount and the standard food intake amount set. This process may involve complex matching algorithms and scoring functions, such as the nonlinear scoring function described above. Then, processing module 2 determines the first real-time eating score number based on the first physiological indicator and the first eating standard score. This step may require the use of the physiological indicator adjustment function mentioned above to fully consider the impact of the physiological indicator.

[0071] Subsequently, the processing module 2 generates a first total eating score based on the first food intake amount and the first physiological indicator. This process involves the application of a comprehensive evaluation function, which requires the processing module 2 to have a strong computing capability.

[0072] Processing module 2 is also responsible for obtaining the second food intake and second physiological index of the patient within a predetermined time, and repeating the above process to generate multiple second food intake standard scores, determine the second real-time food intake score number, and generate a second total food intake score. This repeated processing design enables the system to comprehensively evaluate the patient's food intake status in different time periods.

[0073] The output module 3 is responsible for generating and displaying the final result. Specifically, the output module 3 generates a patient's eating balance score within a predetermined time according to the first real-time eating score, the first total eating score, the second real-time eating score, and the second total eating score. The calculation process of this score follows the formula described above, which requires the output module 3 to have certain mathematical calculation capabilities.

[0074] In a preferred embodiment of the present invention, the system may further include a display module 4 and an alarm module 5. The display module 4 is used to present the processing results to medical staff in an intuitive manner, which may include digital display, chart display, etc. For example, the display module 4 can generate a 24-hour eating curve chart to intuitively display the patient's eating time distribution and food intake changes.

[0075] The alarm module 5 is responsible for promptly reminding medical staff when abnormal conditions are found. For example, when the patient's food intake deviates significantly from the standard value, or the eating balance score is lower than the preset threshold, the alarm module 5 may issue an audible and visual alarm or push a notification to the medical staff's mobile device.

[0076] In addition, the system also includes a data storage module 6 and a communication module 7. The data storage module 6 is used to save the patient's historical eating data and scoring results, supporting long-term trend analysis and personalized program formulation. The communication module 7 is responsible for data exchange with other systems in the hospital, such as uploading the scoring results to the central nursing management system, or obtaining the latest dietary recommendations from the nutrition department.

[0077] It is worth noting that the modules of this system are not simply linearly related, but form a complex network structure. For example, processing module 2 may call acquisition module 1 multiple times as needed to supplement data, or adjust the processing strategy based on the feedback of the output module 3. This flexible interaction between modules enables the system to adapt to complex and changing clinical environments.

[0078] In practical applications, this system can be integrated into smart beds or mobile nursing vehicles to become a powerful assistant for medical staff. Through continuous monitoring and intelligent analysis of patients' eating conditions, this system can greatly improve the accuracy and efficiency of surgical care and provide strong support for patients' recovery.

[0079] In general, the intelligent data processing system based on surgical care provided by the present invention realizes a comprehensive and accurate assessment of the patient's eating status through modular design and the application of intelligent algorithms. This not only reduces the workload of medical staff, but also provides reliable data support for the formulation of personalized nursing plans, and is expected to play an important role in improving the quality of nursing and accelerating patient recovery.

[0080] In order to verify the superiority of the intelligent data processing method and system based on surgical nursing of the present invention, a three-month clinical application study was conducted in the surgical ward of a tertiary hospital. The subjects were 100 patients undergoing abdominal surgery, with an age range of 45-65 years old, including 58 males and 42 females. These patients were randomly divided into three groups: Example 1 group (using the method of the present invention, 40 people), Comparative Example 1 group (using the traditional manual recording method, 30 people) and Comparative Example 2 group (using a simple electronic recording system, 30 people).

[0081] The first embodiment group used the intelligent data processing system of the present invention, which includes an intelligent bedside terminal, a wearable physiological monitoring device and a central nursing station. The intelligent bedside terminal is equipped with a high-precision electronic scale that can automatically record the patient's food intake. The wearable device monitors the patient's blood sugar, blood pressure and other physiological indicators in real time. The central nursing station is responsible for comprehensive data analysis and result display.

[0082] Comparative Example 1 used the traditional paper recording method, with nurses manually recording the patient's eating status and physiological indicators. Comparative Example 2 used a simple electronic recording system that could electronically record the amount of food intake and physiological indicators, but lacked intelligent analysis functions.

[0083] The focus is on the following indicators: accuracy of food intake records, completeness of physiological indicator monitoring, nursing staff work efficiency, degree of improvement in patient nutritional status and patient satisfaction. The detection methods of these indicators are as follows:

[0084] 1. Accuracy of food intake records: Randomly check 10 food intake records of each patient and compare them with the actual food intake.

[0085] 2. Completeness of physiological indicator monitoring: Check the ratio of the number of monitoring times to the actual number of monitoring times within a week.

[0086] 3. Nursing staff work efficiency: Count the time spent by nursing staff on feeding monitoring and recording.

[0087] 4. The degree of improvement in the nutritional status of patients: Compare the changes in serum albumin levels at admission and discharge.

[0088] 5. Patient satisfaction: A questionnaire survey was conducted upon discharge using a 5-point Likert scale.

[0089] After three months of implementation and data collection, the results are shown in Table 1:

[0090] Table 1. Comparison of test results of Example 1, Comparative Example 1 and Comparative Example 2 , As can be seen from the results in Table 1, the method of the present invention has shown obvious advantages in all aspects. First, in terms of the accuracy of food intake recording, the embodiment group achieved a high accuracy of 98.5%, which is much higher than the 85.7% of traditional manual recording and the 92.3% of simple electronic recording. This is due to the high-precision electronic scale and automatic recording system used in the present invention, which effectively reduces human errors.

[0091] Secondly, in terms of the completeness of physiological indicator monitoring, the implementation group also performed far ahead, reaching 99.2% completeness. This is mainly due to the continuous monitoring function of the wearable device, which greatly reduces the number of missed measurements. In contrast, traditional methods and simple electronic recording systems are inevitably prone to omissions due to their reliance on manual operations.

[0092] In terms of nursing staff work efficiency, the advantages of the present invention are more prominent. The nursing staff of the embodiment group only needs to spend 15.3 minutes per day on each patient's feeding monitoring and recording, while the traditional method takes 42.7 minutes and the simple electronic recording system takes 28.9 minutes. This means that the present invention can save a lot of time for nursing staff, allowing them to devote more energy to other important nursing work.

[0093] More importantly, the present invention has a significant effect on improving the nutritional status of patients. The serum albumin level of patients in the embodiment group increased by an average of 5.2 g / L, which is much higher than that of the other two groups. This shows that the intelligent data processing system of the present invention is not only a recording tool, but also can formulate a more reasonable nutrition plan for patients through accurate monitoring and analysis, thereby effectively improving their nutritional status.

[0094] Finally, in terms of patient satisfaction, the implementation group also achieved the highest score (4.7 points). This reflects the patients' high recognition of this intelligent and precise nursing method. Many patients said in their feedback that real-time feeding feedback and physiological index monitoring made them feel more secure and more actively involved in their own recovery process.

[0095] In summary, the intelligent data processing method and system based on surgical nursing of the present invention have shown significant advantages in improving nursing accuracy, improving work efficiency, and improving patient prognosis. This intelligent solution can not only reduce the workload of medical staff, but also provide patients with more accurate and personalized nursing services. With the increasing prominence of the shortage of medical resources, the present invention is expected to play an important role in improving medical quality and optimizing resource allocation, and contribute to promoting the development of smart medicine.

[0096] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, replacement, and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent data processing method based on surgical nursing, characterized in that: include: The acquisition steps include: Obtaining the patient's first food intake and first physiological index within a predetermined time; Get the set of standard food intake corresponding to a preset single meal; Processing steps include: generating a plurality of first eating standard scores based on the first food intake amount and the standard food intake amount set; Determining a first real-time eating score according to the first physiological indicator and the first eating standard score; generating a first total eating score based on the first food intake amount and the first physiological indicator; Obtaining a second food intake and a second physiological index of the patient within a predetermined time; generating a plurality of second eating standard scores based on the second eating amount and the standard eating amount set; Determining a second real-time eating score according to the second physiological indicator and the second eating standard score; generating a second total eating score based on the second food intake amount and the second physiological indicator; Output steps include: A patient's eating balance score within a predetermined time is generated according to the first real-time eating score number, the first total eating score, the second real-time eating score number and the second total eating score.

2. The method according to claim 1, characterized in that The processing steps also include: Based on the first food intake amount, dividing the standard food intake amount set into N first food intake amounts and corresponding n food intake standard scores; Determine a set of eating standards corresponding to the first eating amounts according to the N first eating amounts; Based on the eating standard set corresponding to the first eating amount, gain processing is performed on each first eating standard score to obtain a second eating standard score.

3. The method according to claim 2, characterized in that The processing steps also include: Based on each of the second eating standard scores, obtaining n first physiological indicators corresponding to the N first eating amounts; Determine, according to the n first physiological indicators corresponding to each of the first food intake amounts, an average value of the n first physiological indicators of each of the first food intake amounts; Generate n first real-time eating standard scores based on each first physiological indicator mean value and the second eating standard score; Determining a first real-time eating score set for a first eating amount set according to the n first real-time eating standard scores; Based on the first real-time eating score set, a first real-time eating score number of the patient within a predetermined time is determined.

4. The method according to claim 3, characterized in that The processing steps also include: Based on each standard food intake amount in the standard food intake amount set, obtaining a corresponding food intake reference threshold; According to the reference eating threshold, setting an eating threshold corresponding to each standard eating amount based on the first eating amount; Determining an eating threshold corresponding to each first eating amount in the first eating amount set based on a matching result between the first eating amount and the standard eating amount set; Obtaining an eating score for each first food intake amount according to the first eating standard score, the eating threshold, and the first real-time eating score number corresponding to each first food intake amount; generating a total eating score of the first food intake set based on the eating score of each of the first food intake amounts; A first total eating score of the patient within a predetermined time is determined according to the total eating score of the first eating amount set.

5. The method according to claim 4, characterized in that The formula for generating the total eating score of the first eating amount set is: , in, is the total eating score of the first eating amount set, is the food intake coefficient, is the number of first food intake in the first food intake set, is the first eating standard score corresponding to the qth first eating amount, is the eating threshold corresponding to the qth first eating amount, is the first real-time eating score corresponding to the qth first eating amount, is the reference value of the first eating standard score corresponding to the qth first eating amount.

6. The method according to claim 1, characterized in that The processing steps also include: Based on each of the second eating standard scores, obtaining n second physiological indicators corresponding to each second eating amount in the second eating amount set; Determine, according to the n second physiological indicators corresponding to each of the second food intake amounts, an average value of the n second physiological indicators of each of the second food intake amounts; Generate n second real-time eating standard scores based on each second physiological indicator mean value and the second eating standard score; Determining a second real-time eating score set for a second eating amount according to the n second real-time eating standard scores; Based on the second real-time eating score set, a second real-time eating score number of the patient within a predetermined time is determined.

7. The method according to claim 6, characterized in that The processing steps also include: Based on each standard food intake amount in the standard food intake amount set, obtaining a corresponding food intake reference threshold; According to the reference eating threshold, setting an eating threshold corresponding to each standard eating amount based on the second eating amount; Based on the matching result between the second food intake amount and the standard food intake amount set, determining the food intake threshold value corresponding to each second food intake amount in the second food intake amount set; Obtaining an eating score for each second eating amount according to the second eating standard score, the eating threshold, and the second real-time eating score number corresponding to each second eating amount; generating a total eating score of the second food intake amount based on the eating score of each of the second food intake amounts; A second total eating score of the patient within a predetermined time is determined according to the total eating score of the second food intake amount.

8. The method according to claim 7, characterized in that The formula for generating the total eating score of the second eating amount is: , in, is the total eating score of the second eating amount, is the food intake coefficient, is the number of second food intake in the second food intake set, is the second eating standard score corresponding to the r-th second eating amount, is the feeding threshold corresponding to the rth second feeding amount, is the second real-time eating score corresponding to the r-th second eating amount, is the reference value of the second eating standard score corresponding to the r-th second eating amount.

9. The method according to claim 1, characterized in that: The formula for generating the patient's eating balance score within a predetermined time in the output step is: , in, Score the patient's eating balance within a predetermined time. is the number of first difference values ​​in the first difference set, is the number of second difference values ​​in the second difference value set, is the absolute value of the i-th first difference, is the minimum absolute value of all first difference values ​​in the first difference set, is the preset maximum value. For the The absolute value of the second difference, is the minimum value of the absolute values ​​of all second difference values ​​in the second difference value set, is the preset maximum value.

10. An intelligent data processing system based on surgical care for executing the method according to any one of claims 1 to 9, characterized in that: include: Get modules for: Obtaining the patient's first food intake and first physiological index within a predetermined time; Get the set of standard food intake corresponding to a preset single meal; Processing modules for: generating a plurality of first eating standard scores based on the first food intake amount and the standard food intake amount set; Determining a first real-time eating score according to the first physiological indicator and the first eating standard score; generating a first total eating score based on the first food intake amount and the first physiological indicator; Obtaining a second food intake and a second physiological index of the patient within a predetermined time; generating a plurality of second eating standard scores based on the second eating amount and the standard eating amount set; Determining a second real-time eating score according to the second physiological indicator and the second eating standard score; generating a second total eating score based on the second food intake amount and the second physiological indicator; Output modules for: A patient's eating balance score within a predetermined time is generated according to the first real-time eating score number, the first total eating score, the second real-time eating score number and the second total eating score.

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