Medical dressing change system for pediatric surgery

Abstract

The invention discloses a medical dressing change system for pediatric surgery, relates to the field of clinical medicine, solves the problem of poor application effect of the medical dressing change system for pediatric surgery, and comprises a data acquisition module which is used for respectively acquiring an age value, an affected area value and an affected depth difference index of a pediatric patient to obtain surgical condition data of the patient, the data analysis module is used for obtaining the disease classification data of the pediatric patient and the disease severity coefficient of the patient by analyzing the surgical disease data of the patient, and obtaining the disease analysis data of the patient, and the dressing change frequency module is used for analyzing the disease analysis data of the patient to obtain the predicted dressing change frequency of the patient. According to the method, the medical materials can be reasonably distributed by automatically determining the medicine changing frequency, medical resource waste caused by excessive medicine changing is avoided, and the utilization efficiency of medical resources is improved.

Description

Technical Field

[0001] The invention belongs to the field of clinical medicine, relates to data analysis technology, and specifically is a pediatric surgical dressing change system. Background Art

[0002] The existing pediatric surgical dressing change system has the following defects when changing dressings for pediatric patients: 1. The existing pediatric surgical dressing change system cannot predict the dressing change frequency of pediatric patients in advance, resulting in a lack of predictability in the treatment process of pediatric patients, which can easily lead to poor treatment effects;

[0003] 2. Some pediatric surgical dressing systems can easily lead to untimely dressing changes, thereby increasing the patient's pain and discomfort and affecting the emotional and mental health of pediatric patients.

[0004] To this end, we propose a pediatric surgical dressing system. Summary of the invention

[0005] The present invention aims at the deficiencies in the prior art and aims to provide a pediatric surgical dressing change system. The present invention obtains the surgical condition data of the patient based on the age value, the area value of the affected area and the depth difference index of the affected area, obtains the patient's condition severity coefficient according to the analysis of the patient's surgical condition data, obtains the patient's condition severity coefficient threshold and compares the patient's condition severity coefficient with the patient's condition severity coefficient, divides the patient into pediatric patients in a first condition interval and pediatric patients in a second condition interval according to the comparison result, obtains pediatric patient condition classification data, defines the pediatric patient condition classification data and the patient's condition severity coefficient as patient condition analysis data, obtains historical sample cases for pediatric patients in the first condition interval and pediatric patients in the second condition interval in the pediatric patient condition classification data, obtains the dressing change frequency of the pediatric patient by analyzing the historical sample cases and combining the patient's surgical condition data, obtains the first patient's expected dressing change frequency and the second patient's expected dressing change frequency, and changes dressings for the pediatric patient according to the first patient's expected dressing change frequency and the second patient's expected dressing change frequency.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: The specific working process of each module of a pediatric surgical dressing system is as follows: Data acquisition module: used to obtain the age value, affected area area value and affected area depth difference index of the pediatric patient, and obtain the patient's surgical condition data; Data analysis module: used to obtain the patient's condition severity coefficient according to the patient's surgical condition data analysis, obtain the patient's condition severity coefficient threshold and compare the patient's condition severity coefficient with the patient's condition severity coefficient, divide the patient into pediatric patients in the first condition interval and pediatric patients in the second condition interval according to the comparison result, obtain pediatric patient condition classification data, and define the pediatric patient condition classification data and the patient's condition severity coefficient as patient condition analysis data; Dressing change frequency module: used to obtain historical sample cases for pediatric patients in the first condition interval and pediatric patients in the second condition interval in the pediatric patient condition classification data, and obtain the dressing change frequency of pediatric patients by analyzing the historical sample cases and combining the patient's surgical condition data, to obtain the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient, and change dressings for pediatric patients based on the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient.

[0007] Furthermore, the data acquisition module acquires the patient's surgical condition data as follows: Each time a dressing is changed for a pediatric patient, an image of the affected area of ​​the patient is obtained to obtain a surgical dressing change image; In the surgical dressing change image, the location of the child's affected area is marked to obtain the trauma marked area; The area of ​​the affected area corresponding to the pediatric patient is acquired according to the surgical dressing change image to obtain the area value of the affected area; According to the surgical dressing change images, pixel depth analysis is performed on the affected area corresponding to the pediatric patient to obtain the depth difference index of the affected area; The age value of the pediatric patient is obtained to obtain the age value of the pediatric patient; The pediatric patient's age value, affected area area value and affected area depth difference index were defined as the patient's surgical condition data.

[0008] Furthermore, the data acquisition module acquires the area value of the affected area as follows: Define multiple reference frames of the same size, and use the multiple reference frames to fill the wound marking area until the reference frames are used to achieve layer coverage of the wound marking area; Obtaining the number of the benchmark reference frames filled in the wound marking area, and obtaining the reference frame filling number value; The area value corresponding to the benchmark reference frame in the surgical dressing change image is obtained to obtain the area value of the benchmark reference frame; Acquire the scale factor between the surgical dressing change image and the real scene through the first data acquisition device to obtain the image real scale factor; The area value of the affected area is obtained by calculating the area value of the reference frame, the filling quantity value of the reference frame and the image actual scale factor; Calculate the area value of the affected area.

[0009] Furthermore, the data acquisition module acquires the depth difference index of the affected area, as follows: In the wound annotation area in the surgical dressing change image, m pixels at different positions are selected as characteristic pixels of the affected area, and they are named as the first characteristic pixel of the affected area to the mth characteristic pixel of the affected area respectively; The pixel depths corresponding to the first affected area characteristic pixel point to the mth affected area characteristic pixel point are respectively acquired to obtain the first characteristic pixel depth value to the mth characteristic pixel depth value; Compare the first characteristic pixel depth value to the mth characteristic pixel depth value, mark the characteristic pixel depth value with the largest value as the peak characteristic pixel depth value, mark the characteristic pixel depth value with the smallest value as the valley characteristic pixel depth value, calculate the difference between the peak characteristic pixel depth value and the valley characteristic pixel depth value, and take the absolute value of the obtained difference to obtain the characteristic pixel depth peak-to-valley difference; Calculate the average of the first characteristic pixel depth value to the mth characteristic pixel depth value to obtain an average characteristic pixel depth value; The pixel depth stability value is obtained by calculating the first characteristic pixel depth value to the mth characteristic pixel depth value and the average characteristic pixel depth value; Calculate pixel depth stability values; The depth difference index of the affected area is obtained by calculating the pixel depth stability value and the peak-to-valley difference of the characteristic pixel depth; The depth difference index of the affected area was calculated.

[0010] Furthermore, the data analysis module acquires the patient's condition analysis data as follows: Obtain the surgical condition data of the patient, and obtain the age value, affected area area value and affected area depth difference index of the pediatric patient according to the surgical condition data of the patient; The patient's disease severity coefficient is obtained by calculating the age value, the area value of the affected area, and the difference index of the affected area depth; Calculate the patient's disease severity coefficient; Obtaining a threshold value of a patient's condition severity coefficient, comparing the patient's condition severity coefficient with the patient's condition severity coefficient threshold value, and obtaining pediatric patient condition classification data; The pediatric patients' condition classification data and the patients' condition severity coefficient were defined as the patients' condition analysis data.

[0011] Furthermore, the data analysis module obtains the patient's condition severity coefficient threshold, as follows: The age threshold, affected area area threshold, and affected area depth difference index threshold of pediatric patients were obtained respectively; The threshold of the patient's disease severity coefficient is obtained by calculating the age threshold of the pediatric patient, the threshold of the affected area area, and the threshold of the affected area depth difference index; Calculate the patient's condition severity coefficient threshold.

[0012] Furthermore, the data analysis module compares the patient's disease severity coefficient with the patient's disease severity coefficient threshold, as follows: When the patient's condition severity coefficient is greater than or equal to the patient's condition severity coefficient threshold, the pediatric patient is judged to be a pediatric patient in the first condition interval; When the patient's condition severity coefficient is less than the patient's condition severity coefficient threshold, the pediatric patient is determined to be a pediatric patient in the second condition interval.

[0013] Furthermore, the dressing change frequency module performs dressing changes on pediatric patients as follows: Obtain patient condition analysis data, and obtain pediatric patient condition classification data and patient condition severity coefficient based on the patient condition analysis data; The estimated dressing change frequency corresponding to the pediatric patient in the first condition interval is obtained to obtain the estimated dressing change frequency of the first patient; Obtaining the expected dressing change frequency corresponding to the pediatric patient in the second condition interval to obtain the expected dressing change frequency for the second patient; The pediatric patients in the first condition interval are given a dressing change according to the frequency of dressing change predicted for the first patients, and the pediatric patients in the second condition interval are given a dressing change according to the frequency of dressing change predicted for the second patients.

[0014] Furthermore, the dressing change frequency module obtains the estimated dressing change frequency of the first patient, as follows: Randomly select multiple pediatric patients in the first condition interval from the historical admission records of the medical institution as sample pediatric patients; Obtain the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtain multiple sample dressing change frequencies, calculate the average of the multiple sample dressing change frequencies, and obtain the sample average dressing change frequency; The expected dressing change frequency for the first patient is obtained by calculating the sample average dressing change frequency and the patient's condition severity coefficient.

[0015] Furthermore, the dressing change frequency module obtains the estimated dressing change frequency of the second patient, as follows: Randomly select multiple pediatric patients in the second condition interval from the historical admission records of the medical institution as sample pediatric patients; Obtain the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtain multiple sample dressing change frequencies, calculate the average of the multiple sample dressing change frequencies, and obtain the sample average dressing change frequency; The average dressing change frequency of the sample and the patient's condition severity coefficient are calculated to obtain the expected dressing change frequency for the second patient.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention can divide patients into pediatric patients in the first disease range and pediatric patients in the second disease range. For pediatric patients in different ranges, different patient samples are taken to analyze the expected dressing change frequency of patients, which can effectively improve the accuracy and pertinence of the dressing change time in pediatric surgery; 2. The present invention can reasonably allocate medical materials by automatically determining the frequency of dressing changes, avoid the waste of medical resources caused by excessive dressing changes, improve the utilization efficiency of medical resources, and at the same time, provide guarantee for the timeliness of dressing changes for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 is the overall system block diagram of the present invention; Figure 2 It is a diagram of the implementation steps of the present invention; Figure 3 It is a schematic diagram of the surgical dressing change image in the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0020] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a pediatric surgical dressing system, comprising: A data acquisition module, a dressing change frequency module, a dressing change end module and a server, wherein the data acquisition module, the dressing change frequency module and the dressing change end module are respectively connected to the server, and the server controls the data acquisition module, the dressing change frequency module and the dressing change end module respectively; The data acquisition module obtains the age value, the area value of the affected area and the depth difference index of the affected area of ​​the pediatric patient to obtain the surgical condition data of the patient; During each dressing change for a pediatric patient, an image of the affected area of ​​the patient is acquired by a first data acquisition device to obtain a surgical dressing change image; In the surgical dressing change image, the location of the child's affected area is marked to obtain the trauma marked area; The area of ​​the affected area corresponding to the pediatric patient is acquired according to the surgical dressing change image to obtain the area value of the affected area; It should be noted here that: The first data acquisition device involved here is an AR camera; The details are as follows: See also Figure 3 , defining multiple reference frames of the same size, and using the multiple reference frames to fill the wound marking area, until the reference frames are used to achieve layer coverage of the wound marking area; Obtaining the number of the benchmark reference frames filled in the wound marking area, and obtaining the reference frame filling number value; The area value corresponding to the benchmark reference frame in the surgical dressing change image is obtained to obtain the area value of the benchmark reference frame; Acquire the scale factor between the surgical dressing change image and the real scene through the first data acquisition device to obtain the image real scale factor; It should be noted here that: In this application, the image reality scale factor refers to the ratio between the virtual size of an object in the image and its actual size in the real world. This scale factor is used to convert the measurement value in the image into the actual physical size. The area value of the affected area is obtained by calculating the area value of the reference frame, the filling quantity value of the reference frame and the image actual scale factor; Calculate the area value of the affected area, the specific formula is as follows: ; Among them, Hqm is the area value of the affected area, Jzm is the area value of the reference frame, Tcs is the filling quantity value of the reference frame, and Bly is the image reality scale factor; According to the surgical dressing change images, pixel depth analysis is performed on the affected area corresponding to the pediatric patient to obtain the depth difference index of the affected area; The details are as follows: In the wound annotation area in the surgical dressing change image, m pixels at different positions are selected as characteristic pixels of the affected area, and they are named as the first characteristic pixel of the affected area to the mth characteristic pixel of the affected area respectively; It should be noted here that: In this application, m is the number value corresponding to the characteristic pixel points of the affected area, and m is an integer greater than 0; The pixel depths corresponding to the first affected area characteristic pixel point to the mth affected area characteristic pixel point are respectively acquired to obtain the first characteristic pixel depth value to the mth characteristic pixel depth value; Compare the first characteristic pixel depth value to the mth characteristic pixel depth value, mark the characteristic pixel depth value with the largest value as the peak characteristic pixel depth value, mark the characteristic pixel depth value with the smallest value as the valley characteristic pixel depth value, calculate the difference between the peak characteristic pixel depth value and the valley characteristic pixel depth value, and take the absolute value of the obtained difference to obtain the characteristic pixel depth peak-to-valley difference; Calculate the average of the first characteristic pixel depth value to the mth characteristic pixel depth value to obtain an average characteristic pixel depth value; The pixel depth stability value is obtained by calculating the first characteristic pixel depth value to the mth characteristic pixel depth value and the average characteristic pixel depth value; The pixel depth stability value is calculated, and the specific formula is configured as follows: ; Wherein, Wdx is the pixel depth stability value, Xs1 to Xsm are the first characteristic pixel depth value to the mth characteristic pixel depth value, Xsp is the average characteristic pixel depth value, and m is the number value corresponding to the characteristic pixel points in the affected area; The depth difference index of the affected area is obtained by calculating the pixel depth stability value and the peak-to-valley difference of the characteristic pixel depth; The depth difference index of the affected area is calculated, and the specific formula is as follows: ; Among them, Scy is the depth difference index of the affected area, Wdx is the pixel depth stability value, and Fgc is the peak-to-valley difference of the characteristic pixel depth; The age value of the pediatric patient is obtained to obtain the age value of the pediatric patient; The age value, affected area area value and affected area depth difference index of pediatric patients were defined as the surgical condition data of patients; The data acquisition module acquires the patient's surgical condition data and transmits it to the dressing change frequency module; The data analysis module obtains the patient's condition severity coefficient according to the patient's surgical condition data analysis, obtains the patient's condition severity coefficient threshold and compares the patient's condition severity coefficient with the patient's condition severity coefficient, divides the patient into a first condition interval pediatric patient and a second condition interval pediatric patient according to the comparison result, obtains pediatric patient condition classification data, and defines the pediatric patient condition classification data and the patient's condition severity coefficient as patient condition analysis data; Obtain the surgical condition data of the patient, and obtain the age value, affected area area value and affected area depth difference index of the pediatric patient according to the surgical condition data of the patient; The patient's disease severity coefficient is obtained by calculating the age value, the area value of the affected area, and the difference index of the affected area depth; The patient's condition severity coefficient is calculated, and the specific formula configuration is as follows: ; Among them, Hyz is the patient's disease severity coefficient, Scy is the depth difference index of the affected area, Hqm is the area value of the affected area, and Hnl is the age value of the pediatric patient; Obtaining a threshold value of a patient's condition severity coefficient, comparing the patient's condition severity coefficient with the patient's condition severity coefficient threshold value, and obtaining pediatric patient condition classification data; The details are as follows: The age threshold, affected area area threshold, and affected area depth difference index threshold of pediatric patients were obtained respectively; It should be noted here that: The age threshold, affected area threshold, and affected area depth difference index threshold for pediatric patients involved here are professionally set based on historical reception experience; The threshold of the patient's disease severity coefficient is obtained by calculating the age threshold of the pediatric patient, the threshold of the affected area area, and the threshold of the affected area depth difference index; The patient's condition severity coefficient threshold is calculated, and the specific formula configuration is as follows: ; Among them, Hyzy is the threshold of the patient's disease severity coefficient, Scyy is the threshold of the depth difference index of the affected area, Hqmy is the threshold of the affected area area, and Hnly is the age threshold of pediatric patients; The numerical comparison process is as follows: When the patient's condition severity coefficient is greater than or equal to the patient's condition severity coefficient threshold, the pediatric patient is judged to be a pediatric patient in the first condition interval; When the patient's condition severity coefficient is less than the patient's condition severity coefficient threshold, the pediatric patient is determined to be a pediatric patient in the second condition interval; It should be noted here that: The surgical injuries of pediatric patients in the first condition range involved here are greater than those of pediatric patients in the second condition range; The pediatric patients' condition classification data and the patients' condition severity coefficient were defined as the patients' condition analysis data; The data analysis module acquires the patient's condition analysis data and transmits it to the dressing change frequency module; The dressing change frequency module obtains historical sample cases for pediatric patients in the first condition interval and pediatric patients in the second condition interval in the pediatric patient condition classification data, and obtains the dressing change frequency of pediatric patients by analyzing the historical sample cases and combining the surgical condition data of the patients, and obtains the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient, and changes the dressing of the pediatric patient according to the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient; Obtain patient condition analysis data, and obtain pediatric patient condition classification data and patient condition severity coefficient based on the patient condition analysis data; The estimated dressing change frequency corresponding to the pediatric patient in the first condition interval is obtained to obtain the estimated dressing change frequency of the first patient; The details are as follows: Randomly select multiple pediatric patients in the first condition interval from the historical admission records of the medical institution as sample pediatric patients; Obtain the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtain multiple sample dressing change frequencies, calculate the average of the multiple sample dressing change frequencies, and obtain the sample average dressing change frequency; It should be noted here that: The same stage of disease referred to here specifically refers to the same stage of disease development in children. The disease stages designed here include but are not limited to the inflammatory stage (usually within 1-3 days after injury), the proliferative stage (usually within 3-14 days after injury) and the remodeling stage (usually more than 14 days after injury); The average dressing change frequency of the sample and the patient's disease severity coefficient are calculated to obtain the first patient's expected dressing change frequency; Calculate the expected dressing change frequency for the first patient. The specific formula is configured as follows: ; Among them, Yyp1 is the expected dressing change frequency of the first patient, Yph is the average dressing change frequency of the sample, Hyz is the patient's disease severity coefficient, a1 is the set proportional coefficient, and a1 is greater than 0; Obtaining the expected dressing change frequency corresponding to the pediatric patient in the second condition interval to obtain the expected dressing change frequency for the second patient; Randomly select multiple pediatric patients in the second condition interval from the historical admission records of the medical institution as sample pediatric patients; Obtain the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtain multiple sample dressing change frequencies, calculate the average of the multiple sample dressing change frequencies, and obtain the sample average dressing change frequency; The average dressing change frequency of the sample and the patient's disease severity coefficient are calculated to obtain the second patient's expected dressing change frequency; Calculate the expected dressing change frequency for the second patient. The specific formula is configured as follows: ; Among them, Yyp2 is the expected dressing change frequency of the second patient, Yph is the average dressing change frequency of the sample, Hyz is the patient's disease severity coefficient, a1 is the set proportional coefficient, and a1 is greater than 0; The pediatric patients in the first condition interval are given a dressing change according to the frequency of dressing change predicted for the first patients, and the pediatric patients in the second condition interval are given a dressing change according to the frequency of dressing change predicted for the second patients.

[0021] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0022] Embodiment 2 See also Figure 2 Based on another concept of the same invention, a pediatric surgical dressing change method is now proposed for application in a pediatric surgical dressing change system, the medical dressing change method comprising the following steps: Step S1: respectively obtaining the age value, the affected area area value and the affected area depth difference index of the pediatric patient to obtain the surgical condition data of the patient; Step S11: During each dressing change for a pediatric patient, an image of the affected area of ​​the patient is acquired by a first data acquisition device to obtain a surgical dressing change image; Step S12: in the surgical dressing change image, marking the location of the affected area of ​​the child to obtain a wound marking area; Step S13: acquiring the area of ​​the affected area corresponding to the pediatric patient according to the surgical dressing change image to obtain a numerical value of the affected area; The step S13 further includes the following specific steps: Step S131: defining a plurality of reference frames of the same size in the surgical dressing change image, and using the plurality of reference frames to fill the wound marking area, until the reference frames are used to achieve layer coverage of the wound marking area; Step S132: obtaining the number of the base reference frames filled in the wound marking area, and obtaining a reference frame filling number value; Step S133: acquiring the area value corresponding to the benchmark reference frame in the surgical dressing change image to obtain the area value of the benchmark reference frame; Step S134: acquiring a scale factor between the surgical dressing change image and the real scene through a first data acquisition device to obtain an image real scale factor; Step S135: Calculate the area value of the affected area by using the area value of the reference frame, the filling quantity value of the reference frame and the image reality scale factor; Calculate the area value of the affected area, the specific formula is as follows: ; Among them, Hqm is the area value of the affected area, Jzm is the area value of the reference frame, Tcs is the filling quantity value of the reference frame, and Bly is the image reality scale factor; Step S14: performing pixel depth analysis on the affected area corresponding to the pediatric patient according to the surgical dressing change image to obtain a depth difference index of the affected area; The step S14 further includes the following specific steps: Step S141: in the wound annotated area in the surgical dressing change image, m pixels at different positions are selected as the affected area characteristic pixels, and they are named as the first affected area characteristic pixel point to the mth affected area characteristic pixel point respectively; Step S142: acquiring pixel depths corresponding to the first affected area characteristic pixel point to the mth affected area characteristic pixel point respectively, and obtaining first characteristic pixel depth values ​​to mth characteristic pixel depth values; Step S143: comparing the first feature pixel depth value to the mth feature pixel depth value, marking the feature pixel depth value with the largest value as the peak feature pixel depth value, marking the feature pixel depth value with the smallest value as the valley feature pixel depth value, calculating the difference between the peak feature pixel depth value and the valley feature pixel depth value, and taking the absolute value of the obtained difference to obtain the peak-to-valley difference of the feature pixel depth; Step S144: averaging the first feature pixel depth value to the mth feature pixel depth value to obtain an average feature pixel depth value; Step S145: obtaining a pixel depth stability value by calculating the first characteristic pixel depth value to the mth characteristic pixel depth value and the average characteristic pixel depth value; The pixel depth stability value is calculated, and the specific formula is configured as follows: ; Wherein, Wdx is the pixel depth stability value, Xs1 to Xsm are the first characteristic pixel depth value to the mth characteristic pixel depth value, Xsp is the average characteristic pixel depth value, and m is the number value corresponding to the characteristic pixel points in the affected area; Step S15: Calculating the pixel depth stability value and the characteristic pixel depth peak-to-valley difference to obtain the affected area depth difference index; The depth difference index of the affected area is calculated, and the specific formula is as follows: ; Among them, Scy is the depth difference index of the affected area, Wdx is the pixel depth stability value, and Fgc is the peak-to-valley difference of the characteristic pixel depth; Step S16: Acquire the age value of the pediatric patient to obtain the age value of the pediatric patient; Step S17: defining the age value, the affected area area value and the affected area depth difference index of the pediatric patient as the patient's surgical condition data; Step S2: Analyze the patient's surgical condition data to obtain the patient's condition severity coefficient, obtain the patient's condition severity coefficient threshold and compare the patient's condition severity coefficient with the patient's condition severity coefficient, divide the patient into a first condition interval pediatric patient and a second condition interval pediatric patient according to the comparison result, obtain pediatric patient condition classification data, and define the pediatric patient condition classification data and the patient's condition severity coefficient as patient condition analysis data; Step S21: Obtaining surgical condition data of the patient, and obtaining the age value, affected area area value and affected area depth difference index of the pediatric patient according to the surgical condition data of the patient; Step S22: Calculate the patient's age value, the affected area area value, and the affected area depth difference index to obtain the patient's disease severity coefficient; The patient's condition severity coefficient is calculated, and the specific formula configuration is as follows: ; Among them, Hyz is the patient's disease severity coefficient, Scy is the depth difference index of the affected area, Hqm is the area value of the affected area, and Hnl is the age value of the pediatric patient; Step S23: obtaining a threshold value of a patient's condition severity coefficient, comparing the patient's condition severity coefficient with the threshold value of the patient's condition severity coefficient, and obtaining pediatric patient condition classification data; The step S23 further includes the following specific steps: respectively obtaining a pediatric patient age threshold, an affected area area threshold, and an affected area depth difference index threshold; Step S231: Calculating the age threshold of the pediatric patient, the affected area area threshold, and the affected area depth difference index threshold to obtain the patient's condition severity coefficient threshold; The patient's condition severity coefficient threshold is calculated, and the specific formula configuration is as follows: ; Among them, Hyzy is the threshold of the patient's disease severity coefficient, Scyy is the threshold of the depth difference index of the affected area, Hqmy is the threshold of the affected area area, and Hnly is the age threshold of pediatric patients; Step S232: when the patient's condition severity coefficient is greater than or equal to the patient's condition severity coefficient threshold, the pediatric patient is determined to be a pediatric patient in the first condition interval; Step S233: when the patient's condition severity coefficient is less than the patient's condition severity coefficient threshold, the pediatric patient is determined to be a pediatric patient in the second condition interval; Step S24: defining the pediatric patient's condition classification data and the patient's condition severity coefficient as the patient's condition analysis data; Step S3: Obtain historical sample cases for pediatric patients in the first condition interval and pediatric patients in the second condition interval in the pediatric patient condition classification data, and obtain the dressing change frequency of the pediatric patients by analyzing the historical sample cases and combining the surgical condition data of the patients, to obtain the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient, and change the dressing of the pediatric patients according to the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient; Step S31: Acquire patient condition analysis data, and acquire pediatric patient condition classification data and patient condition severity coefficient according to the patient condition analysis data; Step S32: acquiring the expected dressing change frequency corresponding to the pediatric patient in the first condition interval to obtain the expected dressing change frequency for the first patient; The step S32 further includes the following specific steps: Step S321: randomly selecting a plurality of pediatric patients in the first condition interval from the historical consultation records of the medical institution as sample pediatric patients; Step S322: respectively obtaining the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtaining multiple sample dressing change frequencies, and calculating the average of the multiple sample dressing change frequencies to obtain the sample average dressing change frequency; Step S323: Calculating the average dressing change frequency of the samples and the patient's disease severity coefficient to obtain the first patient's estimated dressing change frequency; Calculate the expected dressing change frequency for the first patient. The specific formula is configured as follows: ; Among them, Yyp1 is the expected dressing change frequency of the first patient, Yph is the average dressing change frequency of the sample, Hyz is the patient's disease severity coefficient, a1 is the set proportional coefficient, and a1 is greater than 0; Step S33: acquiring the estimated dressing change frequency corresponding to the pediatric patient in the second condition interval to obtain the estimated dressing change frequency for the second patient; The step S33 further includes the following specific steps: Step S331: randomly selecting a plurality of pediatric patients in the second condition interval from the historical consultation records of the medical institution as sample pediatric patients; Step S332: respectively obtaining the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtaining multiple sample dressing change frequencies, and calculating the average of the multiple sample dressing change frequencies to obtain the sample average dressing change frequency; Step S333: Calculating the average dressing change frequency of the samples and the patient's disease severity coefficient to obtain the second patient's estimated dressing change frequency; Calculate the expected dressing change frequency for the second patient. The specific formula is configured as follows: ; Among them, Yyp2 is the expected dressing change frequency of the second patient, Yph is the average dressing change frequency of the sample, Hyz is the patient's disease severity coefficient, a1 is the set proportional coefficient, and a1 is greater than 0; Step S34: changing the dressing of the pediatric patient in the first condition interval according to the expected dressing change frequency of the first patient; Step S35: changing the dressing for the pediatric patient in the second condition interval according to the estimated dressing change frequency for the second patient.

[0023] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A pediatric surgical dressing system, characterized in that: include: Data acquisition module: used to obtain the age value, affected area area value and affected area depth difference index of the pediatric patient, and obtain the patient's surgical condition data; Data analysis module: used to obtain the patient's condition severity coefficient according to the patient's surgical condition data analysis, obtain the patient's condition severity coefficient threshold and compare the patient's condition severity coefficient with the patient's condition severity coefficient, divide the patient into pediatric patients in the first condition interval and pediatric patients in the second condition interval according to the comparison result, obtain pediatric patient condition classification data, and define the pediatric patient condition classification data and the patient's condition severity coefficient as patient condition analysis data; Dressing change frequency module: used to obtain historical sample cases for pediatric patients in the first condition interval and pediatric patients in the second condition interval in the pediatric patient condition classification data, and obtain the dressing change frequency of pediatric patients by analyzing the historical sample cases and combining the patient's surgical condition data to obtain the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient, and change dressings for pediatric patients based on the expected dressing change frequency of the first patient and the expected dressing change frequency of the second patient.

2. A pediatric surgical dressing system according to claim 1, characterized in that: The data acquisition module acquires the surgical condition data of the patient, as follows: Obtain images of the affected area of ​​the child and obtain surgical dressing change images; In the surgical dressing change image, the location of the child's affected area is marked to obtain the trauma marked area; The area of ​​the affected area corresponding to the pediatric patient is acquired according to the surgical dressing change image to obtain the area value of the affected area; According to the surgical dressing change images, pixel depth analysis is performed on the affected area corresponding to the pediatric patient to obtain the depth difference index of the affected area; The age value of the pediatric patient is obtained to obtain the age value of the pediatric patient; The pediatric patient's age value, affected area area value and affected area depth difference index were defined as the patient's surgical condition data.

3. A pediatric surgical dressing system according to claim 2, characterized in that: The data acquisition module acquires the area value of the affected area, specifically as follows: Define multiple reference frames of the same size, and use the multiple reference frames to fill the wound marking area until the reference frames are used to achieve layer coverage of the wound marking area; Obtaining the number of the benchmark reference frames filled in the wound marking area, and obtaining the reference frame filling number value; The area value corresponding to the benchmark reference frame in the surgical dressing change image is obtained to obtain the area value of the benchmark reference frame; Obtain the scale factor between the surgical dressing change image and the real scene, and obtain the image real scale factor; The area value of the affected area is obtained by calculating the area value of the reference frame, the filling quantity value of the reference frame and the image actual scale factor; Calculate the area value of the affected area.

4. A pediatric surgical dressing system according to claim 2, characterized in that: The data acquisition module acquires the depth difference index of the affected area, as follows: In the wound annotation area in the surgical dressing change image, m pixels at different positions are selected as the characteristic pixels of the affected area, and they are named as the first characteristic pixel of the affected area to the mth characteristic pixel of the affected area respectively; The pixel depths corresponding to the first affected area characteristic pixel point to the mth affected area characteristic pixel point are respectively acquired to obtain the first characteristic pixel depth value to the mth characteristic pixel depth value; Compare the first characteristic pixel depth value to the mth characteristic pixel depth value, mark the characteristic pixel depth value with the largest value as the peak characteristic pixel depth value, mark the characteristic pixel depth value with the smallest value as the valley characteristic pixel depth value, calculate the difference between the peak characteristic pixel depth value and the valley characteristic pixel depth value, and take the absolute value of the obtained difference to obtain the characteristic pixel depth peak-to-valley difference; Calculate the average of the first characteristic pixel depth value to the mth characteristic pixel depth value to obtain an average characteristic pixel depth value; The pixel depth stability value is obtained by calculating the first characteristic pixel depth value to the mth characteristic pixel depth value and the average characteristic pixel depth value; Calculate pixel depth stability values; The depth difference index of the affected area is obtained by calculating the pixel depth stability value and the peak-to-valley difference of the characteristic pixel depth; The depth difference index of the affected area was calculated.

5. A pediatric surgical dressing system according to claim 1, characterized in that: The data analysis module acquires the patient's condition analysis data, as follows: Obtain the surgical condition data of the patient, and obtain the age value, affected area area value and affected area depth difference index of the pediatric patient according to the surgical condition data of the patient; The patient's disease severity coefficient is obtained by calculating the age value, the area value of the affected area, and the difference index of the affected area depth; Calculate the patient's disease severity coefficient; Obtaining a threshold value of a patient's condition severity coefficient, comparing the patient's condition severity coefficient with the patient's condition severity coefficient threshold value, and obtaining pediatric patient condition classification data; The pediatric patients' condition classification data and the patients' condition severity coefficient were defined as the patients' condition analysis data.

6. A pediatric surgical dressing system according to claim 5, characterized in that: The data analysis module obtains the patient's condition severity coefficient threshold, as follows: The age threshold, affected area area threshold, and affected area depth difference index threshold of pediatric patients were obtained respectively; The threshold of the patient's disease severity coefficient is obtained by calculating the age threshold of the pediatric patient, the threshold of the affected area area, and the threshold of the affected area depth difference index; Calculate the patient's condition severity coefficient threshold.

7. A pediatric surgical dressing system according to claim 5, characterized in that: The data analysis module compares the patient's disease severity coefficient with the patient's disease severity coefficient threshold, as follows: When the patient's condition severity coefficient is greater than or equal to the patient's condition severity coefficient threshold, the pediatric patient is judged to be a pediatric patient in the first condition interval; When the patient's condition severity coefficient is less than the patient's condition severity coefficient threshold, the pediatric patient is determined to be a pediatric patient in the second condition interval.

8. A pediatric surgical dressing system according to claim 1, characterized in that: The dressing change frequency module performs dressing changes on pediatric patients as follows: Obtain patient condition analysis data, and obtain pediatric patient condition classification data and patient condition severity coefficient based on the patient condition analysis data; Obtaining the expected dressing change frequency corresponding to the pediatric patient in the first condition interval to obtain the expected dressing change frequency of the first patient; Obtaining the expected dressing change frequency corresponding to the pediatric patient in the second condition interval to obtain the expected dressing change frequency for the second patient; The pediatric patients in the first condition interval are given a dressing change according to the frequency of dressing change predicted for the first patients, and the pediatric patients in the second condition interval are given a dressing change according to the frequency of dressing change predicted for the second patients.

9. A pediatric surgical dressing system according to claim 8, characterized in that: The dressing change frequency module obtains the estimated dressing change frequency of the first patient, as follows: Randomly select multiple pediatric patients in the first condition interval from the historical admission records of the medical institution as sample pediatric patients; Obtain the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtain multiple sample dressing change frequencies, calculate the average of the multiple sample dressing change frequencies, and obtain the sample average dressing change frequency; The expected dressing change frequency for the first patient is obtained by calculating the sample average dressing change frequency and the patient's condition severity coefficient.

10. A pediatric surgical dressing system according to claim 8, characterized in that: The dressing change frequency module obtains the estimated dressing change frequency of the second patient, as follows: Randomly select multiple pediatric patients in the second condition interval from the historical admission records of the medical institutions as sample pediatric patients; Obtain the dressing change frequency of each sample pediatric patient at the same stage of the patient's condition, obtain multiple sample dressing change frequencies, calculate the average of the multiple sample dressing change frequencies, and obtain the sample average dressing change frequency; The average dressing change frequency of the sample and the patient's condition severity coefficient are calculated to obtain the expected dressing change frequency for the second patient.

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