Drainage data statistical method and system of medical drainage ball

Abstract

The invention discloses a drainage data statistical method and system for a medical drainage ball, and the method comprises the steps: carrying out the automatic conversion of a to-be-drained drainage volume of the medical drainage ball, which is set by a user, into a drainage weight through a density conversion algorithm; according to the target drainage weight data, combining a preset drainage weight-negative pressure relation model, and dynamically matching a negative pressure value of a corresponding medical drainage ball; according to the matched negative pressure value, the corresponding extrusion depth is converted in real time through an extrusion depth-negative pressure relation curve, and the converted target extrusion depth is obtained; according to the target extrusion depth, the extrusion depth of the drainage ball is automatically adjusted through an electronic control device; according to the real-time drainage weight and the negative pressure value of the medical drainage ball, a drainage data report is generated through a data statistics and analysis model. According to the embodiment of the invention, the drainage efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and particularly relates to a method and system for statistical analysis of drainage data of a medical drainage ball. Background Art

[0002] In the modern medical field, as a common drainage device, medical drainage balls are widely used in wound drainage of postoperative patients and drainage of body cavity fluids. Its main purpose is to effectively drain excess fluid in the body (such as pus, blood or other fluids) through the negative pressure principle, relieve the pain of patients, and promote wound healing. Traditional medical drainage balls usually rely on manual monitoring of the volume of drained fluid, lacking real-time data feedback and automatic adjustment mechanisms, which pose potential medical risks and operational inconveniences.

[0003] With the continuous progress of medical technology and the development of artificial intelligence technology, more and more intelligent medical devices have begun to be integrated into clinical practice. Especially in the field of drainage, how to statistically analyze and adjust the drainage weight and negative pressure value in real time and accurately, and strive to achieve precise drainage and optimize the treatment effect has become an important research direction in the industry. Existing medical drainage systems often cannot take into account the complexity of the relationship between drainage weight and negative pressure, easily leading to insufficient or excessive drainage, causing discomfort or complications to patients. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for statistical analysis of drainage data of a medical drainage ball to solve the deficiencies in the prior art and improve the efficiency and accuracy of drainage.

[0005] An embodiment of the present application provides a method for statistical analysis of drainage data of a medical drainage ball, the method comprising:

[0006] According to the drainage volume to be drained of the medical drainage ball set by the user, automatically convert it into drainage weight by using a density conversion algorithm, wherein the density conversion algorithm converts the drainage volume into drainage weight in real time by introducing the density parameter of the drainage fluid to generate target drainage weight data;

[0007] According to the target drainage weight data, dynamically match the corresponding negative pressure value of the medical drainage ball in combination with a preset drainage weight-negative pressure relationship model, wherein the drainage weight-negative pressure relationship model is generated by training according to historical data by introducing a machine learning algorithm;

[0008] According to the matched negative pressure value, use the extrusion depth-negative pressure relationship curve to convert it into the corresponding extrusion depth in real time to obtain the converted target extrusion depth, wherein the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor;

[0009] According to the target extrusion depth, the extrusion depth of the drainage ball is automatically adjusted by an electronic control device. The electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect;

[0010] According to the real-time drainage weight and negative pressure value of the medical drainage ball, a drainage data report is generated using a data statistics and analysis model. The data statistics and analysis model generates a curve graph of the change in the volume of the drained fluid over time and a negative pressure adjustment record form by introducing time series analysis and visualization techniques, which are used by medical staff to evaluate the drainage effect.

[0011] Optionally, according to the target drainage weight data, in combination with a preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched. The drainage weight-negative pressure relationship model is generated by training according to historical data by introducing machine learning algorithms, including:

[0012] Load a preset drainage weight-negative pressure relationship model. The drainage weight-negative pressure relationship model is generated by training according to historical drainage data through random forest or neural network algorithms. The historical drainage data includes historical drainage weight and corresponding historical negative pressure values;

[0013] According to the target drainage weight data, dynamic matching is performed using the drainage weight-negative pressure relationship model. The target drainage weight data is input into the drainage weight-negative pressure relationship model, and through forward propagation calculation, the corresponding negative pressure value is generated.

[0014] Optionally, according to the matched negative pressure value, it is converted into the corresponding extrusion depth in real time using an extrusion depth-negative pressure relationship curve to obtain the converted target extrusion depth. The extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor, including:

[0015] Load a preset extrusion depth-negative pressure relationship curve, where the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure through a pressure sensor and a displacement sensor;

[0016] According to the matched negative pressure value, real-time conversion is performed using the extrusion depth-negative pressure relationship curve. The corresponding extrusion depth is calculated through linear interpolation or spline interpolation algorithms to obtain the target extrusion depth.

[0017] Optionally, according to the target extrusion depth, the extrusion depth of the drainage ball is automatically adjusted by an electronic control device. The electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect, including:

[0018] Initialize an electronic control device, which includes a servo motor and a feedback control mechanism. The servo motor monitors the extrusion depth and extrusion force in real time through a position encoder and a torque sensor, and the feedback control mechanism dynamically adjusts the motion parameters of the servo motor through a PID controller;

[0019] According to the target extrusion depth, automatically adjust the extrusion depth of the drainage ball using the servo motor, and monitor the current extrusion depth in real time through the position encoder;

[0020] Calculate the deviation between the current extrusion depth and the target extrusion depth, and generate an adjustment signal using the PID controller;

[0021] According to the adjustment signal, control the motion of the servo motor and gradually adjust the extrusion depth to the target extrusion depth.

[0022] Optionally, according to the real-time drainage weight and negative pressure value of the medical drainage ball, generate a drainage data report using a data statistics and analysis model. Among them, the data statistics and analysis model generates a curve graph of the change of the drainage fluid volume over time and a negative pressure adjustment record table by introducing time series analysis and visualization technology, which are used for medical personnel to evaluate the drainage effect, including:

[0023] According to the real-time drainage weight, use the density conversion algorithm to calculate the drainage fluid volume in real time;

[0024] According to the real-time drainage fluid volume and negative pressure value, construct a time series data set, where the drainage fluid volume and negative pressure value at each time point are recorded;

[0025] According to the time series data set, perform statistical analysis using a data statistics and analysis model. Among them, calculate the average value, maximum value, minimum value and change trend of the drainage fluid volume, and calculate the average value, maximum value, minimum value and change trend of the negative pressure value;

[0026] According to the statistical analysis results, generate a drainage data report using visualization technology; among them,

[0027] Generate a curve graph of the change of the drainage fluid volume to show the change trend of the drainage fluid volume;

[0028] Generate a negative pressure adjustment record table to show the adjustment record of the negative pressure value;

[0029] Output the drainage data report, which includes a curve graph of the change of the drainage fluid volume and a negative pressure adjustment record table, and is used for medical personnel to evaluate the drainage effect.

[0030] Another embodiment of the present application provides a drainage data statistics system for a medical drainage ball, and the system includes:

[0031] The first conversion module is used to automatically convert the drainage volume to be drained by the medical drainage ball set by the user into the drainage weight using a density conversion algorithm. Among them, the density conversion algorithm generates target drainage weight data by introducing the density parameter of the drainage fluid and converting the drainage volume into the drainage weight in real time.

[0032] The matching module is used to dynamically match the corresponding negative pressure value of the medical drainage ball according to the target drainage weight data in combination with a preset drainage weight-negative pressure relationship model. Among them, the drainage weight-negative pressure relationship model is generated by training according to historical data by introducing a machine learning algorithm.

[0033] The second conversion module is used to convert the matched negative pressure value into the corresponding extrusion depth in real time using an extrusion depth-negative pressure relationship curve to obtain the converted target extrusion depth. Among them, the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor.

[0034] The adjustment module is used to automatically adjust the extrusion depth of the drainage ball according to the target extrusion depth using an electronic control device. Among them, the electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect.

[0035] The generation module is used to generate a drainage data report according to the real-time drainage weight and negative pressure value of the medical drainage ball using a data statistics and analysis model. Among them, the data statistics and analysis model generates a curve graph of the change in the drainage fluid volume over time and a negative pressure adjustment record form by introducing time series analysis and visualization technology for medical personnel to evaluate the drainage effect.

[0036] Another embodiment of the present application provides a storage medium in which a computer program is stored. Among them, the computer program is set to execute the method described in any one of the above when running.

[0037] Another embodiment of the present application provides an electronic device including a memory and a processor. A computer program is stored in the memory, and the processor is set to run the computer program to execute the method described in any one of the above.

[0038] Compared with the prior art, a method for statistical analysis of drainage data of a medical drainage ball provided by the present invention automatically converts the drainage volume to be drained of the medical drainage ball set by the user into drainage weight by using a density conversion algorithm; according to the target drainage weight data, in combination with a preset drainage weight-negative pressure relationship model, dynamically matches the corresponding negative pressure value of the medical drainage ball; according to the matched negative pressure value, uses a squeezing depth-negative pressure relationship curve to convert it into the corresponding squeezing depth in real time to obtain the converted target squeezing depth; according to the target squeezing depth, automatically adjusts the squeezing depth of the drainage ball by using an electronic control device; generates a drainage data report by using a data statistical and analysis model according to the real-time drainage weight and negative pressure value of the medical drainage ball, so as to improve the efficiency and accuracy of drainage. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a hardware structure block diagram of a computer terminal for a method for statistical analysis of drainage data of a medical drainage ball provided by an embodiment of the present invention;

[0040] Figure 2 It is a flowchart of a method for statistical analysis of drainage data of a medical drainage ball provided by an embodiment of the present invention;

[0041] Figure 3 It is a structural schematic diagram of a system for statistical analysis of drainage data of a medical drainage ball provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention.

[0043] An embodiment of the present invention first provides a method for statistical analysis of drainage data of a medical drainage ball, and this method can be applied to an electronic device, such as a computer terminal, specifically, such as an ordinary computer, etc.

[0044] The following takes running on a computer terminal as an example to describe it in detail. Figure 1 It is a hardware structure block diagram of a computer terminal for a method for statistical analysis of drainage data of a medical drainage ball provided by an embodiment of the present invention. As Figure 1 shown, this computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory may include a non-volatile storage medium and an internal memory.

[0045] The non-volatile storage medium can store an operating system and a computer program. This computer program includes program instructions, and when the program instructions are executed, the processor can execute any method for statistical analysis of drainage data of a medical drainage ball.

[0046] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0047] The internal memory provides an environment for the operation of a computer program in a non-volatile storage medium. When the computer program is executed by a processor, the processor can be caused to execute any statistical method for drainage data of a medical drainage ball.

[0048] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 1 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0049] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0050] See Figure 2 , an embodiment of the present invention provides a method for statistically analyzing drainage data of a medical drainage ball, which may include the following steps:

[0051] S201, according to the drainage volume to be drained of the medical drainage ball set by the user, automatically convert it into drainage weight by using a density conversion algorithm. Among them, the density conversion algorithm generates target drainage weight data by introducing the density parameter of the drainage fluid and converting the drainage volume into drainage weight in real time;

[0052] In the first step of this method, the user sets the drainage volume to be drained, and the system will automatically convert the drainage volume into drainage weight by using a density conversion algorithm. Specifically, the density conversion algorithm generates the corresponding drainage weight data in real time by introducing the density parameter of the drainage fluid (for example, the typical density value for different liquid types). This process not only involves simple multiplication operations, but also needs to consider the possible density changes of different liquids during the drainage process. Through this method, the system can accurately generate target drainage weight data, providing an accurate reference for subsequent negative pressure value matching and extrusion depth adjustment.

[0053] This step plays an important role in medical drainage management. By automatically converting the drainage volume into drainage weight, medical staff can more accurately assess the patient's drainage needs, thus avoiding medical risks such as insufficient drainage or excessive drainage. In addition, accurate drainage weight data can help the system dynamically match the required negative pressure value according to the preset drainage weight-negative pressure relationship model. The realization of this process not only improves the scientificity and accuracy of drainage, but also provides data support for medical decision-making, helping to optimize the patient's treatment effect and improve the overall nursing quality.

[0054] In the first step of this method, the user first sets the drainage volume to be drained through the control interface of the medical drainage ball. Suppose the volume of the liquid to be drained after surgery for a patient is set to 250 milliliters. Then, after receiving this input, the system will quickly perform subsequent calculations. During this process, the system will consult the density data of the drainage fluid. Suppose the drainage fluid is blood, and its average density is 1.06 grams per milliliter. To enhance the accuracy of the algorithm, a data table containing the densities of various drainage fluids has been established during the design of the system to meet the needs of different clinical situations.

[0055] Next, the system will use the density conversion algorithm to automatically calculate the drainage weight. The specific calculation process is that the system multiplies the set drainage volume (250 milliliters) by the corresponding drainage fluid density (1.06 grams per milliliter) to obtain the target drainage weight. In this example, the calculation result is 250 milliliters × 1.06 grams per milliliter = 265 grams. Therefore, the system generates the target drainage weight data of 265 grams in real time. The automation of this process not only saves the time of medical staff, but also ensures the accuracy of the calculation results, avoiding errors that may occur due to manual calculation.

[0056] Finally, the target drainage weight data will be transmitted to the subsequent steps to dynamically match the negative pressure value of the corresponding medical drainage ball. Through the drainage weight-negative pressure relationship model, the system can quickly calculate the required negative pressure value according to the model trained with historical data to ensure the optimization of the drainage effect. For example, according to the set target drainage weight of 265 grams, the system obtains the corresponding negative pressure value from the preset model (suppose the required negative pressure calculated by the model is 120 mmHg). In this way, the entire process from setting the drainage volume, calculating the drainage weight to matching the negative pressure value realizes efficient and accurate automatic processing, laying a solid foundation for the smooth progress of subsequent drainage operations.

[0057] S202. According to the target drainage weight data, in combination with a preset drainage weight-negative pressure relationship model, dynamically match the negative pressure value of the corresponding medical drainage ball, wherein the drainage weight-negative pressure relationship model is generated by training with historical data by introducing a machine learning algorithm;

[0058] In this method, based on the target drainage weight data set by the user, the system will perform dynamic matching in combination with a preset drainage weight-negative pressure relationship model to determine the negative pressure value required for the medical drainage ball. This drainage weight-negative pressure relationship model is generated by training according to historical drainage data (such as historical drainage weight and corresponding historical negative pressure values) through the introduction of machine learning algorithms. Through this process, the system can automatically correspond the target drainage weight data with the corresponding negative pressure value, ensuring the efficiency and stability of the drainage process. Especially, the machine learning algorithm can optimize and improve the prediction accuracy of the model during the continuous accumulation of historical data, enabling precise matching of negative pressure in actual operation and meeting clinical requirements.

[0059] Dynamically matching the negative pressure value of the medical drainage ball not only improves the automation degree of the drainage process, but also significantly optimizes the drainage effect. By combining machine learning algorithms, this method can learn and adapt to drainage requirements in different situations based on historical data, reduce the need for manual intervention, and lower the risk of operation errors. Precise negative pressure matching can effectively avoid the phenomena of insufficient drainage or excessive drainage, helping to improve the treatment effect and comfort of patients. In addition, the ability to adjust the negative pressure value in real time enables the system to quickly respond to the real-time feedback of patients, further enhancing the safety and effectiveness of treatment.

[0060] Specifically, a preset drainage weight-negative pressure relationship model can be loaded. The drainage weight-negative pressure relationship model is generated by training according to historical drainage data through random forest or neural network algorithms. The historical drainage data includes historical drainage weight and corresponding historical negative pressure values.

[0061] In this step, the system first loads a preset drainage weight-negative pressure relationship model. This model is generated by training according to historical drainage data through machine learning algorithms, especially random forest or neural network algorithms. The historical drainage data includes past drainage weight and corresponding negative pressure values, which provide the basis for the training and optimization of the model. In this way, the model can learn the complex relationship between drainage weight and negative pressure, and then perform real-time prediction and adjustment in actual applications.

[0062] The significance of this process lies in that with the powerful ability of machine learning, the system can establish complex non-linear relationships and effectively predict the negative pressure values required in different drainage situations. Such a model not only improves the scientificity and reliability of drainage operations, but also provides medical staff with more accurate data support in clinical decision-making. By continuously updating and training the model, it can adapt to various individual differences, making the device perform better in a variety of clinical situations.

[0063] In this step, the system first needs to obtain the preset drainage weight - negative pressure relationship model from the database. This model is generated through machine learning algorithms, especially random forests or neural networks, which can handle complex non - linear relationships and are suitable for dealing with the relationship between drainage weight and negative pressure values. During the training process of the model, the system will utilize historical drainage data, which is sourced from past medical records and involves the drainage weights and corresponding negative pressure values of different patients under different circumstances. Specifically, this includes the weight of the fluid after drainage surgery and the negative pressure value applied under specific conditions.

[0064] During the training process, the historical data is first cleaned and pre - processed to remove outliers and missing data to ensure data quality. When using algorithms such as random forests or neural networks for training, the system will divide the data into a training set and a test set. The former is used for model learning, and the latter is used to verify the accuracy of the model. The training of the model requires multiple iterations, and the algorithm will continuously adjust its own parameters to achieve the optimal fitting effect. The finally generated drainage weight - negative pressure relationship model will be stored in the system and can be called at any time.

[0065] For example, assume that by statistically analyzing the surgical data of the hospital in the past year, it is found that the average drainage weight of a certain type of postoperative drainage fluid is 600 grams, which is common, and its corresponding negative pressure value fluctuates between 90 and 100 millimeters of mercury. Through the above - mentioned training process, the model can finally accurately reflect the appropriate negative pressure value (for example, 95 millimeters of mercury) when the drainage weight is 600 grams. When the system is actually used, it will directly load this model for subsequent drainage applications.

[0066] According to the target drainage weight data, dynamic matching is performed using the drainage weight - negative pressure relationship model. Specifically, the target drainage weight data is input into the drainage weight - negative pressure relationship model, and through forward propagation calculation, the corresponding negative pressure value is generated.

[0067] In this step, the target drainage weight data is input into the already - loaded drainage weight - negative pressure relationship model and processed using the forward propagation algorithm. Through the multi - layer network structure of the algorithm, the input drainage weight data undergoes a series of weighted calculations, and finally the corresponding negative pressure value is output. This forward propagation mechanism can effectively capture the relationship between drainage weight and negative pressure value, making the matching process both fast and accurate.

[0068] This dynamic matching process ensures that the medical drainage ball can quickly adjust the negative pressure under different drainage volumes, thus meeting clinical needs. Through the real - time processing of the target drainage weight data, the medical team can carry out necessary interventions based on real - time feedback, avoiding complications caused by insufficient or excessive negative pressure, and further enhancing patient safety. Precise negative pressure adjustment is also conducive to shortening the drainage time and improving the patient's postoperative recovery.

[0069] In this step, the system inputs the target drainage weight data into the already loaded drainage weight - negative pressure relationship model. This step is crucial because it can directly affect the effectiveness and safety of subsequent operations. Through the forward propagation algorithm, the system combines the features in the historical data and calculates layer by layer the negative pressure value that matches the target drainage weight. This process usually involves the weighted summation of multiple neuron nodes and the application of activation functions to ensure that the finally output negative pressure value can meet the actual clinical needs.

[0070] Specifically, assuming that the target drainage weight set by the user is 350 grams, the system inputs this data into the model. After multiple layers of calculations by the model and combining the internal weight parameters, a corresponding negative pressure value is finally output. For example, after calculation, the model obtains that the reasonable negative pressure value corresponding to a drainage weight of 350 grams is 88 mmHg. The generation of this negative pressure value is based on the previous historical data and the operation results of machine learning algorithms, and can effectively reflect the dynamic requirements in different drainage situations.

[0071] This process of dynamic matching is very crucial in clinical applications because it can ensure that the operating state of the drainage ball matches the actual condition of the patient, thereby optimizing the drainage effect. For example, when the drainage fluid of the patient accumulates too fast, the system can quickly calculate the negative pressure value that needs to be increased, thereby adjusting the operation to ensure the safety and effectiveness of drainage. This real-time and accuracy are undoubtedly important guarantees for medical staff to improve patient safety.

[0072] S203. According to the matched negative pressure value, use the extrusion depth - negative pressure relationship curve to convert it into the corresponding extrusion depth in real time to obtain the converted target extrusion depth, where the extrusion depth - negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor;

[0073] In this step, the system converts the corresponding extrusion depth in real time according to the negative pressure value obtained by the previous matching by using the extrusion depth - negative pressure relationship curve. The extrusion depth - negative pressure relationship curve is established by introducing a pressure sensor and a displacement sensor, and the data collected by these sensors can reflect the quantitative relationship between the extrusion depth and the negative pressure. This relationship curve can be constructed by linear or non-linear methods, so that the system can effectively calculate the required extrusion depth under different negative pressure conditions, thereby promoting the best operating state of the drainage ball.

[0074] This dynamic process of converting the target extrusion depth is crucial for ensuring the effectiveness of medical drainage. Accurate adjustment of the extrusion depth can ensure the stability of the drainage ball during the application of negative pressure, preventing unsmooth drainage or damage caused by insufficient or excessive pressure. This real-time adjustment not only improves the drainage efficiency but also helps to protect the patient's physiological state and avoid potential complications. Establishing a good relationship between the extrusion depth and negative pressure to ensure the safety and effectiveness of the drainage operation is of great significance to the work of medical staff.

[0075] Specifically, a preset extrusion depth-negative pressure relationship curve can be loaded. Among them, the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and negative pressure through a pressure sensor and a displacement sensor.

[0076] In this step, the system initializes and loads a preset extrusion depth-negative pressure relationship curve. This relationship curve is established by pressure sensors and displacement sensors configured in the device. The sensors will monitor the pressure changes and extrusion depth changes inside the drainage ball in real time. By analyzing these data, the system can draw the specific relationship between the extrusion depth and negative pressure, enabling the medical drainage ball to accurately adjust the extrusion depth according to the negative pressure value during actual operation.

[0077] The importance of loading the extrusion depth-negative pressure relationship curve lies in that it provides the necessary basis for subsequent depth adjustment. This relationship curve can help medical devices establish a scientific and reasonable mapping relationship between negative pressure and extrusion depth, thus ensuring accuracy under different operating conditions. With such data support, medical staff can better manage the drainage needs of patients in clinical practice and improve the overall treatment effect.

[0078] In this step, the initial task of the system is to load a preset extrusion depth-negative pressure relationship curve. This curve is established by introducing pressure sensors and displacement sensors in the device. The sensors will monitor the pressure and extrusion depth inside the drainage ball in real time. This process first requires determining the installation positions of the sensors to ensure accurate collection of relevant data. Generally, the pressure sensor will be installed on the pipeline where the drainage liquid flows, and the displacement sensor will be placed on the components directly related to the extrusion mechanism, such as the corresponding part of the extrusion diaphragm, so as to accurately record the change in the extrusion depth of the diaphragm.

[0079] Next, the system calibrates the sensor through a preset test procedure to ensure the accuracy of its measurements. At this point, engineers will conduct a series of standardized tests, such as measuring the extrusion depth under different negative pressures. By gradually adjusting the negative pressure (such as gradually increasing from 50 mmHg to 150 mmHg), the system will read and record the corresponding extrusion depth data at each set negative pressure point. The collected data will provide the basis for subsequent curve generation. After the data recording is completed, the system will use statistical analysis software to generate an extrusion depth-negative pressure relationship curve, which will become an important basis for subsequent steps.

[0080] For example, during the test, suppose that when the negative pressure is 60 mmHg, the recorded squeezing depth is 8 mm; when the negative pressure is 100 mmHg, the squeezing depth is 12 mm. These data will be integrated to form a clear relationship curve, so that the system can quickly query the corresponding squeezing depth according to the real-time negative pressure value during operation. The accurate recording and analysis of data in this process is very important, which ensures that the system can accurately respond to various negative pressure requirements in subsequent operation and promote the drainage ball to work in the best state.

[0081] According to the matched negative pressure value, the extrusion depth-negative pressure relationship curve is used for real-time conversion, wherein the corresponding extrusion depth is calculated by a linear interpolation or spline interpolation algorithm to obtain the target extrusion depth.

[0082] In this step, the system uses the pre-established extrusion depth-negative pressure relationship curve to perform real-time conversion of the extrusion depth based on the matched negative pressure value. This process usually uses linear interpolation or spline interpolation algorithms to help the system deduce the corresponding extrusion depth from the known negative pressure value. For example, if the current negative pressure value is 85 mmHg, the system will find the corresponding extrusion depth on the relationship curve and calculate it. This interpolation method ensures that the calculation of the extrusion depth can be accurate and efficient.

[0083] By converting the extrusion depth in real time, the system can efficiently respond to different negative pressure requirements and ensure the stability of negative pressure during medical drainage. Accurate adjustment of the extrusion depth not only helps to improve drainage efficiency, but also reduces the risks caused by uneven extrusion, such as bleeding or postoperative complications. With the help of interpolation calculation, medical equipment can accurately adapt to different clinical needs, making the drainage process smoother and safer.

[0084] In this step, the system needs to perform real-time conversion based on the negative pressure value obtained from the previous step's matching through the loaded extrusion depth-negative pressure relationship curve to obtain the target extrusion depth. First, the system will find the corresponding interval in the relationship curve according to the current negative pressure value. For example, if the current negative pressure is 85 mmHg, and in the curve, it is known that the extrusion depth corresponding to a negative pressure of 80 mmHg is 9 mm, and the extrusion depth corresponding to a negative pressure of 90 mmHg is 10 mm, the system will compare the current negative pressure value with these two known values to provide a reference for the calculation of the extrusion depth.

[0085] Next, the system uses the method of linear interpolation or spline interpolation for calculation. Linear interpolation is a simple and effective method that directly calculates the required value through the linear relationship between known points. Spline interpolation, on the other hand, provides higher smoothness and can better adapt to complex relationship curves. When using linear interpolation, calculate according to the formula. Given the relationship between the negative pressure value and the known points, the extrusion depth can be obtained. Based on the current negative pressure value of 85 mmHg, the system can calculate the target extrusion depth through the following formula: Target extrusion depth = 9+(85 - 80)*(10 - 9) / (90 - 80)=9.5 mm.

[0086] Through this dynamic calculation, the system can respond in a timely manner to changes in negative pressure, ensuring the normal operation of the drainage ball under different negative pressure conditions. Based on the previous example, assuming that the target extrusion depth needs to be adjusted to 9.5 mm at this time, the system will give feedback to the mechanical components, which means that the extrusion mechanism may need to be fine-tuned by one step to ensure the stability of negative pressure and improve the drainage effect. This efficient real-time conversion ensures the safety and effectiveness of the drainage procedure. Especially in clinical applications, it can timely adapt to the different drainage needs of patients and truly achieve personalized medical care.

[0087] S204, automatically adjust the extrusion depth of the drainage ball according to the target extrusion depth by using an electronic control device, wherein the electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect;

[0088] In this step, the system automatically adjusts the extrusion depth of the medical drainage ball by using an electronic control device according to the target extrusion depth calculated in the early stage. The electronic control device usually includes a servo motor and a feedback control mechanism. A servo motor is a motor that can precisely control position and speed. After receiving an instruction from the system, it will make corresponding adjustments according to the set target extrusion depth. At the same time, the feedback control mechanism will ensure the accuracy of the extrusion amplitude and maintain the required negative pressure effect by monitoring the extrusion depth in real time. This control process ensures the effective operation of the drainage ball under different negative pressure conditions, thereby improving the drainage effect.

[0089] This automated extrusion depth adjustment process is crucial for improving the safety and effectiveness of medical drainage. By adjusting the extrusion depth in real time, it can ensure that the drainage ball maintains an optimal state when applying negative pressure, avoiding poor drainage or tissue damage caused by insufficient negative pressure or excessive extrusion. Achieving this goal not only improves patient comfort but also reduces the risk of postoperative complications. The automated control also reduces human intervention, saving time for medical staff and enhancing medical efficiency.

[0090] Specifically, the electronic control device can be initialized. The electronic control device includes a servo motor and a feedback control mechanism. The servo motor, through a position encoder and a torque sensor, monitors the extrusion depth and extrusion force in real time. The feedback control mechanism, through a PID controller, dynamically adjusts the motion parameters of the servo motor.

[0091] In this initialization step, the electronic control device is first assembled and debugged. The electronic control device mainly consists of a servo motor, a position encoder, a torque sensor, and a PID controller. The servo motor is responsible for precise conversion and movement according to the input instructions, while the position encoder monitors the extrusion depth in real time by sensing the deviation between the current extrusion depth and the set target depth. At the same time, the torque sensor monitors the force applied to the extrusion component to ensure that the applied extrusion force is within a safe range. The role of the PID controller is to continuously obtain the current state information and adjust the motion parameters of the servo motor in real time to ensure rapid and stable adjustment of the extrusion depth.

[0092] The real-time monitoring and control functions of this initialization process are crucial in the medical drainage system. It can ensure precise control of the extrusion depth of the drainage ball, effectively preventing unstable negative pressure and poor drainage caused by inconsistent extrusion depths. Through accurate feedback and adjustment, the system not only improves the drainage effect but can also respond quickly in case of abnormal situations, ensuring the safety and comfort of patients.

[0093] In this initialization step, the combined components of the electronic control device are first designed to work together. The servo motor is the core part of the system, responsible for precise adjustment of the extrusion depth according to the control signal. At the same time, the position encoder is installed on the rotating shaft of the servo motor to monitor the extrusion depth in real time. The torque sensor is attached to the mechanical component of the extrusion ball to monitor the force applied to the extrusion component. The output data of these sensors will be used as feedback signals and provided to the feedback control mechanism.

[0094] During the initialization process, it is first necessary to perform self-checks and calibrations on all sensors to ensure the normal operation of their real-time monitoring functions. For example, the system checks whether the position encoder can accurately return the current extrusion depth data and ensures that the torque sensor can record the actual applied force during the extrusion process. If any abnormalities are detected during the inspection, the system will automatically record and prompt the operator for maintenance or replacement. It is crucial to ensure that all devices are in the best condition before operation to prevent failures during subsequent operations.

[0095] Once all components have been confirmed to be error-free through self-checks, the system will start the PID control algorithm. The PID controller will set the initial motion parameters according to the set target extrusion depth (such as a target of 8 millimeters). At this time, the servo motor will be adjusted to the standby state, ready to respond to new instructions at any time based on real-time feedback. The entire initialization process ensures that the electronic control device has good response capabilities and execution efficiency, laying the foundation for subsequent extrusion depth adjustments.

[0096] According to the target extrusion depth, the servo motor is used to automatically adjust the extrusion depth of the drainage ball, and the current extrusion depth is monitored in real time through the position encoder;

[0097] In this step, the system instructs the servo motor to return to the required extrusion depth according to the pre-set target extrusion depth (for example, 8 millimeters). After receiving the target instruction, the servo motor will quickly start to adjust, and the position encoder will monitor the current extrusion depth in real time to ensure that it is close to the target depth. Through the closed-loop control system, the data of the current extrusion depth is fed back to the PID controller in real time, and the PID controller will dynamically adjust the motion speed and direction of the servo motor according to the deviation between the current extrusion depth and the target extrusion depth.

[0098] This adjustment link is very crucial to ensure that the extrusion depth of the drainage ball is always maintained at the optimal level, thereby improving the efficiency and safety of drainage. Precise depth adjustment can not only effectively manage the stability of negative pressure but also reduce the risk of misoperation caused by human factors. Maintaining an appropriate extrusion depth helps to enhance the overall drainage effect, reduce postoperative complications, and improve the quality of the patient's recovery.

[0099] The key to this step is to use the servo motor to perform precise depth adjustment according to the target extrusion depth set by the system. Assume that the current target depth is 8 millimeters and the current extrusion depth monitored by the position encoder in real time is 6 millimeters. Based on this data, the system will start to calculate the adjustment amplitude. The data transmission in this process is real-time to ensure that the servo motor can respond immediately.

[0100] When the system detects a 2 - millimeter gap between the current extrusion depth and the target extrusion depth, the servo motor will receive an adjustment instruction. The motor starts to rotate and performs the corresponding movement. During this process, the position encoder continuously feeds back the current extrusion depth to the system to ensure that the system can monitor the movement of the motor in real time. If during the adjustment process, the extrusion depth reaches 7 millimeters, the system will continue to compare this new depth with the target depth until the target is finally reached.

[0101] It should be noted that the adjustment of the servo motor is in stages, and the movement speed and acceleration of the motor will be dynamically adjusted by the PID controller according to the feedback data. For example, when it reaches 7 millimeters, the system will reduce the rotation speed of the motor to achieve the final adjustment. By this method of gradually approaching the target depth, it can effectively avoid vibrations or sudden damages caused by too fast adjustment, ensuring the safety and smoothness of the adjustment process of the extrusion depth of the drainage ball.

[0102] Calculate the deviation between the current extrusion depth and the target extrusion depth, and use the PID controller to generate an adjustment signal;

[0103] In this step, the system first needs to compare the difference between the current extrusion depth and the target extrusion depth. This process involves the acquisition and processing of real - time data. Taking the current extrusion depth as 7 millimeters and the target extrusion depth as 8 millimeters as an example, the system will calculate that the deviation is 1 millimeter. The PID controller will calculate the corresponding adjustment signal based on this deviation. The PID controller mainly consists of three parts: proportional, integral, and derivative. The proportional part will generate an immediate adjustment according to the deviation; the integral part will consider past errors to ensure the long - term accuracy of the system; the derivative part can predict future trends and thus make adjustments in advance. Through the comprehensive calculation of these three, the PID controller can generate a precise adjustment signal.

[0104] The main function of this step is to ensure that the extrusion depth of the drainage ball can not only quickly respond to the current state changes but also be kept within the set safety range through precise feedback control, improving the stability and effectiveness of the drainage process. At the same time, the PID controller achieves efficient adjustment in continuous feedback regulation, which can minimize the medical risks caused by operation errors and improve the safety of patients.

[0105] In this step, the system first needs to calculate the deviation between the current extrusion depth and the target extrusion depth. This deviation is calculated through simple mathematical operations. The system will subtract the current depth (assumed to be 7 millimeters) from the target depth (such as 8 millimeters) to obtain a deviation value of 1 millimeter. This value will be the direct basis for generating the adjustment signal.

[0106] Next, through the working mechanism of the PID controller, the system analyzes the deviation value to generate an appropriate adjustment signal. The PID controller is divided into three parts: Proportional (P), Integral (I), and Derivative (D). The proportional part directly considers the current deviation and generates an adjustment signal proportional to the deviation; the integral part focuses on the historical cumulative error to address the persistent deviation; and the derivative part attempts to predict the possible future deviation trend. The combined action of these three parts enables the PID controller to generate an adjustment signal that is both responsive and stable.

[0107] For example, if the proportional gain is set to 2 and the current deviation is 1 mm, the output of the proportional part is 2; if there has been a cumulative deviation in the past few feedbacks, the integral output may also be 1; and if the current output change tends to be stable, the derivative output may be 0. The PID controller adds these three values together to produce the final adjustment signal. This signal is transmitted to the servo motor in real-time so that the motor can adjust the extrusion depth according to the new data and gradually approach the target value.

[0108] Based on the adjustment signal, the movement of the servo motor is controlled to gradually adjust the extrusion depth to the target extrusion depth.

[0109] The last step is to control the servo motor to gradually adjust the extrusion depth of the drainage ball according to the adjustment signal generated by the PID controller until it reaches the target depth. The servo motor starts running according to the received adjustment signal and changes the extrusion depth of the extrusion component by changing the rotation speed and direction. This process is carried out step by step, and the motor carefully adjusts at a certain movement rate to ensure accurate reaching of the target extrusion depth and avoid pressure fluctuations caused by too fast adjustment.

[0110] Through this step, the system can turn the theoretical adjustment of the extrusion depth into actual operation, ensuring that the drainage ball can work effectively at the required depth. This step-by-step adjustment not only maintains the stability of the system but also responds promptly to any emergencies, ensuring no risk of over- or under-application of negative pressure. Ultimately, it ensures a safer and more comfortable experience for the patient during the drainage process.

[0111] In this step, the system will instruct the servo motor to start moving according to the adjustment signal generated by the PID controller to achieve a gradual adjustment of the extrusion depth. Suppose the adjustment signal generated by the PID controller indicates that the extrusion depth needs to be increased to 8 mm, the servo motor will start working and adjust according to the preset speed and direction.

[0112] After receiving the adjustment signal, the servo motor will start rotating quickly, and the real-time monitoring setting ensures that it can accurately adjust the extrusion depth. For example, if the motor is set to a rotation speed of 200 rpm, the motor will start moving the extrusion component at this speed. The position encoder will continuously update the extrusion depth data. Once the set target depth is reached, the motor will immediately decelerate and finally stop running. This real-time monitoring and adjustment keeps the depth in an ideal state.

[0113] In addition, the adjustment process is phased to ensure the smoothness of the process. The motor will gradually reduce the adjustment amplitude according to the real-time feedback. When approaching the target, such as reaching 7.8 mm, the motor may reduce the speed to achieve more delicate control. This step-by-step adjustment method effectively avoids the risk of extrusion damage caused by being too fast, and at the same time ensures that the drainage ball can maintain the best extrusion state, thus maximizing the drainage effect.

[0114] S205, according to the real-time drainage weight and negative pressure value of the medical drainage ball, generate a drainage data report by using a data statistics and analysis model. Among them, the data statistics and analysis model generates a curve graph of the change of the drainage fluid volume over time and a negative pressure adjustment record form by introducing time series analysis and visualization technology, which is used for medical staff to evaluate the drainage effect.

[0115] In this step, the medical drainage ball system will monitor the drainage weight and negative pressure value of the drainage ball in real time and generate a drainage data report by using a data statistics and analysis model. By introducing time series analysis, this model can effectively record the change of the drainage fluid volume over time, thus providing important clinical data for medical staff. Specifically, the system will periodically collect real-time drainage weight data and negative pressure values, and integrate these data together to form a dynamic time series data set. Then, use these data to generate a curve graph and a record form, so that medical staff can intuitively observe and analyze the drainage effect and the adjustment process of the negative pressure.

[0116] The generation of this data report has important clinical significance. By presenting the change curve of the drainage fluid volume over time in a visual way, medical staff can clearly understand the timeliness and effectiveness of the drainage process. This provides data support for evaluating the drainage effect and judging the treatment direction, and at the same time can help doctors identify potential problems, such as unsmooth drainage or unstable negative pressure. In addition, the negative pressure adjustment record form can provide a reference for subsequent clinical decisions, prompting the timely adjustment of the treatment plan, thereby improving the safety and comfort of patients.

[0117] Specifically, according to the real-time drainage weight, the drainage fluid volume can be calculated in real time by using a density conversion algorithm;

[0118] In this step, the system will use a density conversion algorithm to calculate the drainage fluid volume in real time. First, the system collects real-time drainage weight data and calculates based on the known density of the drainage fluid (usually in grams per milliliter). For example, assume the real-time drainage weight monitored by the system is 100 grams, and the density of the drainage fluid is 1.05 grams / ml. By applying the density conversion algorithm, the system can convert the drainage weight into the drainage fluid volume, which is calculated as 100 grams divided by 1.05 grams / ml, approximately 95.24 ml, thus obtaining the drainage fluid volume.

[0119] Calculating the drainage fluid volume in real time is crucial for evaluating the drainage effect. Accurate measurement of the fluid volume can help medical staff promptly understand the drainage status, determine whether the drainage is normal, and whether the drainage plan needs to be adjusted. By monitoring the changes in the drainage volume, medical staff can effectively evaluate the patient's disease progression, ensure that the treatment proceeds as expected, and reduce the occurrence of complications and discomfort.

[0120] In the implementation of this step, the system first needs to obtain real-time drainage weight data. This data is obtained through a weighing sensor installed below the drainage tube, which can monitor the weight of the liquid flowing out of the system in real time. For example, assume the real-time drainage weight measured by a drainage ball during the operation is 150 grams. To convert this weight into the drainage fluid volume, the system needs the known density value of the drainage fluid. Assume the density of the drainage fluid is 1.05 grams / ml, and the system will use the density conversion algorithm for conversion.

[0121] Next, the system will calculate through the formula: drainage fluid volume (ml) = drainage weight (g) / density of drainage fluid (g / ml). Therefore, for 150 grams of drainage fluid, the system will calculate the drainage fluid volume as 150 g / 1.05 g / ml ≈ 142.86 ml. This can ensure the system's real-time and accurate tracking of the drainage volume, ensuring that medical staff can promptly obtain drainage status information.

[0122] Finally, the generated drainage fluid volume data will be automatically updated and stored in the database. Each time new drainage weight is obtained, the system will recalculate and update the corresponding fluid volume data. This process is automated, ensuring that medical staff always have the latest drainage data, thus providing a basis for subsequent evaluation and decision-making.

[0123] Construct a time series data set based on the real-time drainage fluid volume and negative pressure value, where the drainage fluid volume and negative pressure value at each time point are recorded;

[0124] In this step, the system will construct a time series dataset based on the real-time drainage fluid volume and negative pressure value. Each weight and negative pressure data will be recorded as a data point, forming a continuously updated database. For example, assume that at 0 minutes, the drainage fluid volume is 95.24 ml and the negative pressure is 80 mmHg; at 1 minute, the drainage fluid volume is updated to 96.50 ml and the negative pressure is 82 mmHg. The system records these two sets of data to form a data series corresponding to timestamps for subsequent analysis.

[0125] The significance of constructing a time series dataset lies in enabling systematic dynamic monitoring and evaluation of the drainage process. By recording the changes in the drainage fluid volume and negative pressure value, the dataset can help medical staff analyze the temporal change trend of the drainage effect, identify potential problems such as unsmooth drainage or abnormal negative pressure changes, etc., which provides a data basis for doctors to make timely clinical decisions.

[0126] In this step, the system establishes a time series dataset by real-time monitoring and recording the drainage fluid volume and negative pressure data. First, the system needs to obtain the real-time data of the drainage fluid volume and negative pressure through sensors. The drainage fluid volume has been calculated in the previous step, while the negative pressure is real-time monitored through a pressure sensor installed in the drainage system. For example, if within a one-minute time interval, the system records that the drainage fluid volume is 142.86 ml and the negative pressure value is 80 mmHg, this data will be marked as time point T1.

[0127] Next, the system will automatically collect and record these data at set time intervals (such as every minute) to form a time series. As time goes by, for example, after 2 minutes, the system measures that the drainage fluid volume is 145 ml and the negative pressure value is 82 mmHg, this data will be marked as time point T2. In this way, the system will form a dynamic record containing the drainage fluid volume and negative pressure data corresponding to multiple time points.

[0128] Finally, all the recorded data will be stored in a time series database, and each record contains a timestamp, the drainage fluid volume, and the negative pressure value. This structured data can facilitate subsequent analysis and visualization, ensuring that medical staff can easily access and refer to the drainage status at each time point and promptly grasp the real-time condition changes of the patient.

[0129] According to the time series dataset, statistical analysis is carried out using data statistics and analysis models. Among them, calculate the average value, maximum value, minimum value, and change trend of the drainage fluid volume, and calculate the average value, maximum value, minimum value, and change trend of the negative pressure value;

[0130] In this step, the system will perform statistics and analysis using the time - series dataset to calculate various statistical indicators of the drainage fluid volume and negative pressure value. This includes the average value, maximum value, minimum value, and their change trends. For example, if the time - series dataset records the drainage volume data for 10 consecutive times, the system will calculate the average value, maximum value, and minimum value of these data, thereby providing an overview of the overall drainage effect. The same applies to the statistics of the negative pressure value. Through the calculation of these data, medical staff can intuitively understand the overall effect of drainage and the stability of negative pressure.

[0131] The significance of performing statistical analysis is to provide decision - making support for medical staff in a quantitative way. The average value can be used as a reference indicator, and the maximum and minimum values can be used to judge whether the drainage process is stable. The change trend can reveal potential clinical problems. For example, whether it is necessary to adjust the negative pressure or change the settings of the drainage tube to ensure the effectiveness and safety of the overall patient treatment.

[0132] In this step, the system will extract the data of the drainage fluid volume and negative pressure value from the constructed time - series dataset for statistical analysis. First, the system will summarize all the records obtained from the database. For example, if the database records the drainage fluid volume and negative pressure value data every minute in the past 30 minutes, the system will perform statistical operations on these data. By calling the built - in statistical analysis module, the system can easily calculate the average value, maximum value, and minimum value of the drainage fluid volume. For example, assuming that in 30 records, the total volume of the fluid is 4300 milliliters, then the average fluid volume is 4300 milliliters / 30 ≈ 143.33 milliliters.

[0133] Similarly, the system will also calculate the statistical data of the negative pressure value, including the average value, maximum value, and minimum value. Assuming that in the same time period, the highest recorded negative pressure value is 85 mmHg and the lowest is 75 mmHg, then the system will determine that the average negative pressure is (75 + 85) / 2 = 80 mmHg. Through these data, medical staff can evaluate the efficiency of drainage and the stability of negative pressure.

[0134] To further analyze the trend of data changes, the system will use basic trend analysis methods, such as linear regression analysis, to determine the change direction of the drainage fluid volume and negative pressure value. Through statistical and trend analysis, the system will generate a detailed analysis report for medical staff to evaluate the drainage process and its effect.

[0135] According to the results of statistical analysis, a drainage data report is generated using visualization technology; among them, a curve graph of the change in the drainage fluid volume is generated to show the change trend of the drainage fluid volume; a record form of negative pressure adjustment is generated to show the adjustment records of the negative pressure value; the drainage data report is output, and the drainage data report includes a curve graph of the change in the drainage fluid volume and a record form of negative pressure adjustment for medical staff to evaluate the drainage effect.

[0136] In this step, the results of the statistical analysis will be converted into a visual form to generate a drainage data report. Specifically, the system will establish a curve graph of the change in the volume of the drainage fluid, which can show the trend of the change in the volume of the drainage fluid over time and intuitively reflect the stability of the drainage process. In addition, the system will also generate a negative pressure adjustment record table, listing the time points of each negative pressure adjustment and their corresponding values, facilitating comparative analysis by medical staff.

[0137] Visualization technology plays a crucial role in this step. By presenting the statistical results through a graphical interface, medical staff can intuitively understand the change trend of the drainage and the history of negative pressure adjustment. This intuitive information presentation can help medical staff quickly identify problems and formulate corresponding countermeasures, thereby improving the efficiency and accuracy of clinical decision-making.

[0138] In this step, the system will generate a drainage data report using visualization technology based on the previous statistical analysis results. First, the system will generate a curve graph of the change in the volume of the drainage fluid. This graph plots the volume of the drainage fluid as a curve through the change in time series data, with time as the abscissa and the volume of the drainage fluid as the ordinate. For example, assuming that the volume data within the first 30 minutes are 142.86, 145, 143.0, 148.5, and 147.0 milliliters respectively, the system will connect these data points into a curve to intuitively show how the volume of the fluid changes over time.

[0139] In addition, the system will also generate a negative pressure adjustment record table. This table will list the changes in the negative pressure value at each time point. For example, if the negative pressure value gradually adjusts from 80 mmHg to 82 mmHg, the system will record this change in the table, including the time, the negative pressure value, and any relevant notes. In this way, medical staff can clearly understand the history of negative pressure adjustment, providing a data basis for the subsequent revision of the treatment plan.

[0140] Finally, the generated drainage data report will be output in the form of a PDF or a graphical report, facilitating printing or electronic storage by medical staff. The report not only includes the drainage curve graph and the negative pressure adjustment record table, but may also be accompanied by text explanations and conclusions, summarizing the drainage effect of the patient and the recommended subsequent treatment measures. This visual data presentation greatly improves the comprehensibility of information and the efficiency of clinical decision-making.

[0141] It can be seen that according to the drainage volume to be drained by the medical drainage ball set by the user, it is automatically converted into the drainage weight by using the density conversion algorithm; according to the target drainage weight data, in combination with the preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched; according to the matched negative pressure value, using the extrusion depth-negative pressure relationship curve, it is converted into the corresponding extrusion depth in real time to obtain the converted target extrusion depth; according to the target extrusion depth, the extrusion depth of the drainage ball is automatically adjusted by using the electronic control device; according to the real-time drainage weight and negative pressure value of the medical drainage ball, a drainage data report is generated by using the data statistics and analysis model, thereby improving the efficiency and accuracy of drainage.

[0142] Another embodiment of the present invention provides a drainage data statistics system for a medical drainage ball. Refer to Figure 3 , the system may include:

[0143] The first conversion module 301 is configured to automatically convert the drainage volume to be drained by the medical drainage ball set by the user into the drainage weight by using the density conversion algorithm. Among them, the density conversion algorithm converts the drainage volume into the drainage weight in real time by introducing the density parameter of the drainage fluid, and generates the target drainage weight data;

[0144] The matching module 302 is configured to dynamically match the corresponding negative pressure value of the medical drainage ball according to the target drainage weight data in combination with the preset drainage weight-negative pressure relationship model. Among them, the drainage weight-negative pressure relationship model is trained and generated according to historical data by introducing a machine learning algorithm;

[0145] The second conversion module 303 is configured to convert the matched negative pressure value into the corresponding extrusion depth in real time by using the extrusion depth-negative pressure relationship curve to obtain the converted target extrusion depth. Among them, the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor;

[0146] The adjustment module 304 is configured to automatically adjust the extrusion depth of the drainage ball according to the target extrusion depth by using the electronic control device. Among them, the electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect;

[0147] The generation module 305 is configured to generate a drainage data report according to the real-time drainage weight and negative pressure value of the medical drainage ball by using the data statistics and analysis model. Among them, the data statistics and analysis model generates a curve graph of the change of the drainage fluid volume with time and a negative pressure adjustment record form by introducing time series analysis and visualization technology, which is used for medical staff to evaluate the drainage effect.

[0148] It can be seen that according to the drainage volume to be drained by the medical drainage ball set by the user, it is automatically converted into drainage weight by using the density conversion algorithm; according to the target drainage weight data, combined with the preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched; according to the matched negative pressure value, the extrusion depth-negative pressure relationship curve is used to convert it into the corresponding extrusion depth in real time, and the converted target extrusion depth is obtained; according to the target extrusion depth, the electronic control device is used to automatically adjust the extrusion depth of the drainage ball; according to the real-time drainage weight and negative pressure value of the medical drainage ball, a drainage data report is generated by using the data statistics and analysis model, so as to improve the efficiency and accuracy of drainage.

[0149] An embodiment of the present invention also provides a storage medium, in which a computer program is stored, and the computer program is set to execute the steps in any one of the above method embodiments when running.

[0150] Specifically, in this embodiment, the above storage medium can be set to store a computer program for executing the following steps:

[0151] S201, according to the drainage volume to be drained by the medical drainage ball set by the user, it is automatically converted into drainage weight by using the density conversion algorithm, where the density conversion algorithm converts the drainage volume into drainage weight in real time by introducing the density parameter of the drainage fluid, and generates target drainage weight data;

[0152] S202, according to the target drainage weight data, combined with the preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched, where the drainage weight-negative pressure relationship model is trained and generated according to historical data by introducing a machine learning algorithm;

[0153] S203, according to the matched negative pressure value, the extrusion depth-negative pressure relationship curve is used to convert it into the corresponding extrusion depth in real time, and the converted target extrusion depth is obtained, where the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor;

[0154] S204, according to the target extrusion depth, the electronic control device is used to automatically adjust the extrusion depth of the drainage ball, where the electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect;

[0155] S205. Generate a drainage data report based on the real-time drainage weight and negative pressure value of the medical drainage ball. The data statistics and analysis model generates a curve graph showing the change in drainage fluid volume over time and a negative pressure adjustment record form by introducing time series analysis and visualization techniques, which are used by medical staff to evaluate the drainage effect.

[0156] It can be seen that according to the set drainage volume of the medical drainage ball to be drained by the user, the density conversion algorithm is used to automatically convert it into a drainage weight; according to the target drainage weight data, combined with the preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched; according to the matched negative pressure value, the extrusion depth-negative pressure relationship curve is used to convert it into the corresponding extrusion depth in real time to obtain the converted target extrusion depth; according to the target extrusion depth, the electronic control device automatically adjusts the extrusion depth of the drainage ball; according to the real-time drainage weight and negative pressure value of the medical drainage ball, the data statistics and analysis model generates a drainage data report, thereby improving the efficiency and accuracy of drainage.

[0157] The embodiment of the present invention also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0158] Specifically, the above electronic device may further include a transmission device and an input / output device. The transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0159] Specifically, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0160] S201. According to the set drainage volume of the medical drainage ball to be drained by the user, the density conversion algorithm is used to automatically convert it into a drainage weight. The density conversion algorithm generates target drainage weight data by introducing the density parameter of the drainage fluid and converting the drainage volume in real time into a drainage weight.

[0161] S202. According to the target drainage weight data, combined with the preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched. The drainage weight-negative pressure relationship model is generated by training according to historical data by introducing a machine learning algorithm.

[0162] S203. According to the matched negative pressure value, the extrusion depth-negative pressure relationship curve is used to convert it into the corresponding extrusion depth in real time to obtain the converted target extrusion depth. The extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor.

[0163] S204. Automatically adjust the extrusion depth of the drainage ball according to the target extrusion depth by using an electronic control device. The electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect;

[0164] S205. Generate a drainage data report according to the real-time drainage weight and negative pressure value of the medical drainage ball by using a data statistics and analysis model. The data statistics and analysis model generates a curve graph showing the change of the drainage fluid volume over time and a negative pressure adjustment record table by introducing time series analysis and visualization techniques for medical staff to evaluate the drainage effect.

[0165] It can be seen that according to the drainage volume to be drained of the medical drainage ball set by the user, the density conversion algorithm is used to automatically convert it into the drainage weight; according to the target drainage weight data, combined with the preset drainage weight-negative pressure relationship model, the corresponding negative pressure value of the medical drainage ball is dynamically matched; according to the matched negative pressure value, the extrusion depth-negative pressure relationship curve is used to convert it into the corresponding extrusion depth in real time to obtain the converted target extrusion depth; according to the target extrusion depth, the electronic control device is used to automatically adjust the extrusion depth of the drainage ball; according to the real-time drainage weight and negative pressure value of the medical drainage ball, a drainage data report is generated by using a data statistics and analysis model, thereby improving the efficiency and accuracy of drainage.

[0166] The structure, features and function effects of the present invention have been described in detail based on the embodiments shown in the drawings. The above is only the preferred embodiment of the present invention, but the present invention is not limited to the scope defined by the drawings. Any changes made according to the concept of the present invention or modified into equivalent embodiments with equivalent changes still within the spirit covered by the description and the drawings should be within the protection scope of the present invention.

Claims

1. A method for statistically analyzing drainage data of a medical drainage ball, characterized in that, The method includes: According to the drainage volume of the medical drainage ball to be drained set by the user, it is automatically converted into drainage weight by using a density conversion algorithm. Among them, the density conversion algorithm introduces the density parameter of the drainage fluid to convert the drainage volume into drainage weight in real time, generating target drainage weight data; According to the target drainage weight data, combined with a preset drainage weight-negative pressure relationship model, dynamically match the corresponding negative pressure value of the medical drainage ball. Among them, the drainage weight-negative pressure relationship model is generated by training according to historical data by introducing a machine learning algorithm; According to the matched negative pressure value, use the extrusion depth-negative pressure relationship curve to convert it into the corresponding extrusion depth in real time, and obtain the converted target extrusion depth. Among them, the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor; According to the target extrusion depth, use an electronic control device to automatically adjust the extrusion depth of the drainage ball. Among them, the electronic control device introduces a servo motor and a feedback control mechanism to adjust the extrusion depth in real time to ensure the stability of the negative pressure value and the optimization of the drainage effect; According to the real-time drainage weight and negative pressure value of the medical drainage ball, use a data statistics and analysis model to generate a drainage data report. Among them, the data statistics and analysis model generates a curve graph of the change in drainage fluid volume over time and a negative pressure adjustment record form by introducing time series analysis and visualization technology for medical personnel to evaluate the drainage effect.

2. The method according to claim 1, wherein The step of according to the target drainage weight data, combined with a preset drainage weight-negative pressure relationship model, dynamically matching the corresponding negative pressure value of the medical drainage ball, where the drainage weight-negative pressure relationship model is generated by training according to historical data by introducing a machine learning algorithm, includes: Load a preset drainage weight-negative pressure relationship model, which is generated by training according to historical drainage data by using a random forest or neural network algorithm. The historical drainage data includes historical drainage weight and corresponding historical negative pressure values; According to the target drainage weight data, perform dynamic matching by using the drainage weight-negative pressure relationship model. Among them, input the target drainage weight data into the drainage weight-negative pressure relationship model and generate the corresponding negative pressure value through forward propagation calculation.

3. The method according to claim 2, wherein The step of according to the matched negative pressure value, using the extrusion depth-negative pressure relationship curve to convert it into the corresponding extrusion depth in real time, and obtaining the converted target extrusion depth. Among them, the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor, includes: Load a preset extrusion depth-negative pressure relationship curve, where the extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by using a pressure sensor and a displacement sensor; According to the matched negative pressure value, perform real-time conversion by using the extrusion depth-negative pressure relationship curve. Among them, calculate the corresponding extrusion depth through a linear interpolation or spline interpolation algorithm to obtain the target extrusion depth.

4. The method according to claim 3, wherein Automatically adjust the extrusion depth of the drainage ball according to the target extrusion depth by using an electronic control device. The electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect, including: Initialize the electronic control device, which includes a servo motor and a feedback control mechanism. The servo motor monitors the extrusion depth and extrusion force in real time through a position encoder and a torque sensor, and the feedback control mechanism dynamically adjusts the motion parameters of the servo motor through a PID controller; Automatically adjust the extrusion depth of the drainage ball by using the servo motor according to the target extrusion depth, and monitor the current extrusion depth in real time through the position encoder; Calculate the deviation between the current extrusion depth and the target extrusion depth, and generate an adjustment signal by using the PID controller; Control the motion of the servo motor according to the adjustment signal, and gradually adjust the extrusion depth to the target extrusion depth.

5. The method according to claim 4, characterized in that, Generate a drainage data report by using a data statistics and analysis model according to the real-time drainage weight and negative pressure value of the medical drainage ball. The data statistics and analysis model generates a curve graph of the change of the drainage fluid volume over time and a negative pressure adjustment record form by introducing time series analysis and visualization technology, which are used for medical staff to evaluate the drainage effect, including: Calculate the drainage fluid volume in real time by using a density conversion algorithm according to the real-time drainage weight; Construct a time series data set according to the real-time drainage fluid volume and negative pressure value, and record the drainage fluid volume and negative pressure value at each time point; Conduct statistical analysis by using the data statistics and analysis model according to the time series data set, including calculating the average value, maximum value, minimum value and change trend of the drainage fluid volume, and calculating the average value, maximum value, minimum value and change trend of the negative pressure value; Generate a drainage data report by using visualization technology according to the statistical analysis results; among them, Generate a curve graph of the change of the drainage fluid volume to show the change trend of the drainage fluid volume; Generate a negative pressure adjustment record form to show the adjustment record of the negative pressure value; Output the drainage data report, which includes a curve graph of the change of the drainage fluid volume and a negative pressure adjustment record form, and is used for medical staff to evaluate the drainage effect.

6. A drainage data statistics system for a medical drainage ball, characterized in that, The system includes: A first conversion module for automatically converting the drainage volume to be drained of the medical drainage ball set by the user into a drainage weight by using a density conversion algorithm. The density conversion algorithm converts the drainage volume into a drainage weight in real time by introducing the density parameter of the drainage fluid to generate target drainage weight data; A matching module for dynamically matching the corresponding negative pressure value of the medical drainage ball according to the target drainage weight data in combination with a preset drainage weight-negative pressure relationship model. The drainage weight-negative pressure relationship model is trained and generated according to historical data by introducing a machine learning algorithm; A second conversion module for converting the matched negative pressure value into the corresponding extrusion depth in real time by using an extrusion depth-negative pressure relationship curve to obtain the converted target extrusion depth. The extrusion depth-negative pressure relationship curve establishes the corresponding relationship between the extrusion depth and the negative pressure by introducing a pressure sensor and a displacement sensor; Adjustment module, configured to automatically adjust the extrusion depth of the drainage ball according to the target extrusion depth by using an electronic control device. The electronic control device adjusts the extrusion depth in real time by introducing a servo motor and a feedback control mechanism to ensure the stability of the negative pressure value and the optimization of the drainage effect; Generation module, configured to generate a drainage data report according to the real-time drainage weight and negative pressure value of the medical drainage ball by using a data statistics and analysis model. The data statistics and analysis model generates a curve graph of the change in drainage fluid volume over time and a negative pressure adjustment record form by introducing time series analysis and visualization techniques for medical staff to evaluate the drainage effect.

7. The system according to claim 6, characterized in that, The matching module is specifically configured to: Load a preset drainage weight-negative pressure relationship model, which is trained and generated according to historical drainage data by using a random forest or neural network algorithm. The historical drainage data includes historical drainage weight and corresponding historical negative pressure values; Perform dynamic matching by using the drainage weight-negative pressure relationship model according to the target drainage weight data. The target drainage weight data is input into the drainage weight-negative pressure relationship model, and through forward propagation calculation, a corresponding negative pressure value is generated.

8. The system according to claim 7, wherein The second conversion module is specifically configured to: Load a preset extrusion depth-negative pressure relationship curve, where the extrusion depth-negative pressure relationship curve establishes a correspondence between the extrusion depth and the negative pressure through a pressure sensor and a displacement sensor; Perform real-time conversion by using the extrusion depth-negative pressure relationship curve according to the matched negative pressure value. The corresponding extrusion depth is calculated through a linear interpolation or spline interpolation algorithm to obtain the target extrusion depth.

9. A storage medium, characterized in that, A computer program is stored in the storage medium, where the computer program is configured to execute the method according to any one of claims 1-5 when running.

10. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method according to any one of claims 1-5.

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