Intelligent processing system for PICC complication prevention training data

Abstract

The invention discloses an intelligent processing system for PICC (Peripherally Inserted Central Catheter) complication prevention training data, belongs to the technical field of PICC, and is used for solving the problem of high complication occurrence rate of PICC catheterization. Comprising a patient analysis module, a training module and a prevention analysis module. The patient analysis module is used for analyzing the patient information and determining the PICC standard range of the target patient; the training module is used for simulating PICC (Peripherally Inserted Central Catheter) catheterization operation of a target patient and realizing simulation training for the target patient; the prevention analysis module is used for performing complication prevention analysis when a user performs treatment operation on a target patient to obtain an operation analysis result; performing corresponding prevention processing according to an operation analysis result; the PICC catheterization system intelligently assists medical staff in performing PICC catheterization treatment on a patient, and solves the problem that the occurrence rate of PICC complications is high due to maintenance outside a hospital.

Description

Technical Field

[0001] The invention belongs to the field of PICC technology, and in particular is an intelligent processing system for PICC complication prevention training data. Background Art

[0002] In recent years, peripherally inserted central catheter (PICC) has been widely recognized and applied clinically due to its simple operation and safety. It provides patients with a painless infusion channel, which can reduce the pain caused by repeated venous puncture, reduce the nursing workload, avoid drug extravasation, and improve satisfaction. Therefore, it is used for long-term intravenous infusion, total enteral nutrition, blood transfusion, tumor chemotherapy, and infusion for children and elderly patients with poor vascular conditions. During the indwelling process, it is necessary to pay attention to daily observation, routine maintenance once a week, and dressing change as needed in special circumstances. PICC tubes with better clinical care can be retained in the body for up to one year. However, there are some complications during the use of PICC. PICC-related infection is one of the common complications. The risk factor is puncture site infection, such as low immunity, sweat and exudate irritating the puncture site and not changing the dressing in time. In mild cases, the puncture site will be red, swollen, hot, painful, itching and exudate. In severe cases, purulent secretions may occur. Severe cases may lead to catheter-related infection and even life-threatening. Relevant literature reports many complications of PICC tubes, among which the incidence of puncture site infection accounts for more than 4.1%. Therefore, effective care plays an important role in PICC tube puncture site infection.

[0003] However, the traditional PICC catheterization process relies on the experience and feel of medical staff to locate the vein through body surface landmarks and anatomical structures. However, this method has problems such as inaccurate positioning and difficulty in catheterization, which may lead to catheterization failure, complications and patient discomfort. In addition, traditional positioning methods are more difficult for patients with obesity, edema or abnormal anatomical structures. In particular, in the actual treatment process, some patients currently use home treatment, which makes the PICC catheterization process more difficult to control, resulting in the probability of complications at home being much higher than that in the hospital.

[0004] Therefore, how to prevent complications intelligently and effectively has become a problem that needs to be solved at present. Based on this, the present invention provides an intelligent processing system for PICC complication prevention training data. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a PICC complication prevention training data intelligent processing system.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A PICC complication prevention training data intelligent processing system, comprising a patient analysis module, a training module and a prevention analysis module; The patient analysis module is used to analyze patient information and determine the PICC standard range of the target patient, wherein the PICC standard range includes a physiological parameter standard range and an operation parameter standard range.

[0007] Furthermore, the method for determining the standard range of PICC according to the patient information includes: Acquire the PICC medical data corresponding to the target patient during treatment in the hospital, perform standard judgment on the PICC medical data, and obtain the standard judgment result of the PICC medical data, wherein the standard judgment result includes compliance with standard requirements and non-compliance with standard requirements; Marking the PICC medical data that meets the standard requirements as standard material data, and marking the PICC medical data that does not meet the standard requirements as abnormal material data; Identify the initial physiological parameter range and the initial operating parameter range corresponding to the standard material data; identify the abnormal physiological parameter range and the abnormal operating parameter range corresponding to the abnormal material data; According to the abnormal physiological parameter range and the abnormal operating parameter range, the initial physiological parameter range and the initial operating parameter range are adjusted respectively to obtain the PICC standard range of the target patient; The standard optimization data of the target patient is acquired in real time, and the PICC standard range is adjusted in real time according to the standard optimization data.

[0008] Furthermore, the method for standard judgment of PICC medical data includes: A treatment judgment model is established, and the expression of the treatment judgment model is: ; Where: (S, K) is the input data, S is the PICC medical data; K is the standard requirement, and the standard requirement is no complications; S→K means that the corresponding PICC medical data meets the standard requirements; the output data is the treatment judgment value LP(S, K), and the treatment judgment value is 1 or 0; Analyzing the PICC medical data by using the treatment judgment model to obtain a treatment judgment value corresponding to the PICC medical data; When the treatment judgment value is 1, the standard judgment result of the PICC medical data is in compliance with the standard requirements; When the treatment judgment value is 0, the standard judgment result of the PICC medical data is that it does not meet the standard requirements.

[0009] Furthermore, the method for determining the standard range of PICC according to the patient information includes: Preset each patient category and the corresponding PICC standard range for each patient category; Matching is performed according to the patient information of the target patient, and marking the matched PICC standard range as the PICC initial range; The PICC initial range is displayed to the medical staff of the target patient, and the medical staff adjusts the PICC initial range to obtain the PICC standard range used by the target patient.

[0010] The training module is used to simulate the PICC catheterization operation on the target patient, and includes a simulation unit and an authority unit; The simulation unit is used for the user to perform training simulation according to the preset training content, and evaluate the corresponding training results according to the simulated training data recorded during the training process, and the training results include qualified training and unqualified training; The authority unit is used to check the user's treatment authority and obtain the user's authority check result, wherein the authority check result includes authority check pass and authority check fail; When the authority check fails, a warning of restricted treatment will be issued; When the authority check is passed, no corresponding warning operation is performed.

[0011] Furthermore, the training content is PICC catheterization training, and the method of the simulation unit performing the training simulation includes: The platform presets various physiological parameter backgrounds and qualified processing operations under various physiological parameter backgrounds; establishes a catheterization training model for PICC catheterization simulation; When the user is training, the PICC standard range of the target user is obtained and displayed to the user; the physiological parameter background is randomly applied; the user simulates through the catheterization training model under the physiological parameter background, and the corresponding simulation training data is recorded.

[0012] Furthermore, the method for checking the user's treatment authority includes: Acquire user information, and determine whether the user has a treatment authority tag according to the user information; When the user has a treatment authority tag, evaluating whether the user authority verification has passed; When the user does not have a treatment authority label, the training result of the user on the target patient is identified from the user information, and when the training result is qualified, the user authority verification is evaluated as passed; when the training result is unqualified, the user authority verification is evaluated as failed.

[0013] The prevention analysis module is used for the user to perform complication prevention analysis when performing treatment operations on target patients, collect the user's treatment operation data in real time, analyze the treatment operation data, and obtain operation analysis results, which include qualified operation analysis and unqualified operation analysis; Perform corresponding preventive measures based on the operation analysis results.

[0014] Furthermore, before the user performs treatment operations on the target patient, a corresponding operation tutorial is generated according to the PICC standard range of the target patient; When the user performs a treatment operation on a target patient, the corresponding treatment steps are identified in real time according to the treatment operation data, and corresponding operation guidance is provided according to the treatment steps and operation tutorials.

[0015] Furthermore, the method for analyzing the treatment operation data includes: Establishing a potential analysis model; analyzing the treatment operation data through the potential analysis model to obtain complication analysis data, and determining the operation analysis result according to the complication analysis data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes intelligent assistance to medical staff in PICC catheterization of patients through the mutual cooperation between the patient analysis module, the training module and the prevention analysis module, reduces PICC complications caused by various reasons such as misoperation, and especially solves the problem of high incidence of PICC complications caused by out-of-hospital maintenance; provides training functions to facilitate medical staff to have a full understanding of the treatment of target patients, and guarantees the user's operating ability based on authority management; and can provide early warning and intervention measures in time during the actual operation process, helping medical staff to accurately judge the operation risks and intervene, thereby reducing the incidence of PICC complications. Through data-driven intelligent analysis, it is also possible to optimize the training process, improve the operating skills of medical staff, reduce clinical errors, and improve patient safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, a PICC complication prevention training data intelligent processing system includes a patient analysis module, a training module, and a prevention analysis module; The patient analysis module is used to analyze patient information and determine the PICC standard range of the target patient. The PICC standard range includes the standard range of physiological parameters and the standard range of operating parameters. Physiological parameters refer to data such as heart rate, blood pressure, and blood oxygen saturation, and operating parameters refer to data such as catheter insertion depth and angle. The standard range refers to the target patient's physiological parameters or operating parameters within the range required for normal treatment.

[0021] Among them, the patient information includes the patient's name, age, height, weight, gender, condition and other related data. The target patient is the patient who needs treatment analysis.

[0022] In one embodiment, the method for determining the PICC standard range according to patient information includes: Obtain the PICC medical data stored when the target patient is treated in the hospital, and make a standard judgment on the PICC medical data, specifically, judge whether the treatment is normal treatment based on the PICC medical data, and judge based on the complications of the target patient after treatment, and obtain the standard judgment results of each PICC medical data, including whether it meets the standard requirements and whether it does not meet the standard requirements; mark the PICC medical data that meets the standard requirements as standard material data, and mark the PICC medical data that does not meet the standard requirements as abnormal material data; Identify the physiological parameter range and the operating parameter range corresponding to the standard material data, and mark the obtained physiological parameter range and the operating parameter range as an initial physiological parameter range and an initial operating parameter range, respectively, for the purpose of distinguishing them; Identify the physiological parameter range and the operating parameter range corresponding to the abnormal material data, and mark the obtained physiological parameter range and the operating parameter range as an abnormal physiological parameter range and an abnormal operating parameter range, respectively, for the purpose of distinguishing them; According to the abnormal physiological parameter range and the abnormal operation parameter range, the initial physiological parameter range and the initial operation parameter range are adjusted respectively to obtain the PICC standard range of the target patient; And obtain the standard optimization data of the target patients in real time. The standard optimization data is the PICC medical data corresponding to the subsequent hospital treatment or home treatment; the PICC standard range is adjusted in real time according to the standard optimization data.

[0023] In one embodiment, the standard judgment of PICC medical data can also be performed in the following manner: A treatment judgment model is established. The treatment judgment model identifies the relevant data of complications in the PICC medical data to determine whether it meets the standard requirements, that is, if there are complications, it does not meet the standard requirements; the expression of the treatment judgment model is: ; Where: (S, K) is the input data, S is the PICC medical data; K is the standard requirement, the standard requirement is no complications, S→K means that the PICC medical data meets the standard requirements; the output data is the treatment judgment value LP(S, K), the treatment judgment value is 1 or 0; Analyze each PICC medical data through the treatment judgment model to obtain the treatment judgment value corresponding to each PICC medical data; When the treatment judgment value is 1, the standard judgment result is in compliance with the standard requirements; When the treatment judgment value is 0, the standard judgment result is that it does not meet the standard requirements.

[0024] In one embodiment, the initial physiological parameter range and the initial operating parameter range are adjusted according to the abnormal physiological parameter range and the abnormal operating parameter range, that is, they are adjusted according to the existing PICC treatment common sense. For example, if the angle of the initial operating range is 30° to 40° and 45°, they are merged according to normal conditions to obtain 30° to 45°. If it is not certain whether to perform the merge derivation, no derivation is required, and 30° to 40° and 45° are retained as they are. Similarly, for the abnormal operating parameter range, if the parameter is within the initial operating parameter range, it is removed from the initial operating range, and the abnormal parameter range is prohibited by the mark, that is, the operating parameter standard range includes the allowed parameter range and the prohibited parameter range; the same method can be used for physiological parameters, but the physiological parameters are generally set directly according to the existing standard range, which is mainly used to determine the operating parameter standard range according to PICC medical data. In practical applications, intelligent adjustments can be made according to existing intelligent algorithms.

[0025] In one embodiment, the PICC standard range is determined according to the patient information, and each patient can also be classified based on big data and existing classification and clustering methods to form each patient classification, and different patient classifications correspond to different PICC standard ranges; subsequently, matching is performed according to the patient information of the target patient to determine the corresponding PICC standard range; in order to further optimize the PICC standard range, the matched PICC standard range is marked as the PICC initial range, and the PICC initial range is displayed to the medical staff of the target patient, and the medical staff adjusts the PICC initial range according to the actual situation of the target patient, mainly to narrow the corresponding range and obtain the PICC standard range.

[0026] In one embodiment, the PICC standard range is determined according to the patient information, and the PICC standard range may also be determined based on other existing technologies.

[0027] The training module is used to simulate the PICC catheterization operation on the target patient, and includes a simulation unit and an authority unit; The simulation unit is used for users to perform training simulation according to preset training content, and evaluate the corresponding training results according to the simulated training data recorded during the training process, and the training results include qualified training and unqualified training; the user refers to the person who performs PICC catheterization operation, such as medical staff, etc.; the training content, such as catheterization operation, post-catheterization care, etc., generally includes at least the corresponding training content of catheterization operation and post-catheterization care, and the training content is specifically set according to the actual PICC catheterization training needs and existing operation and nursing regulations; The authority unit is used to check the user's treatment authority and obtain the authority check result of the corresponding user, the authority check result includes the authority check passed and the authority check failed; When the permission check fails, a treatment restriction warning is issued, that is, the user is warned not to perform PICC catheterization on the target user; When the authority check is passed, no corresponding warning operation is performed.

[0028] In one embodiment, as training accumulates and technology develops, training optimization can be performed.

[0029] In one embodiment, the simulation unit is set up based on existing technologies, such as establishing a patient model, determining training items according to training content, and setting corresponding simulation operation functions according to each training item, such as selecting the PICC catheter position, and then moving its depth and angle, and dynamically generating changes in physiological parameters under different situations, understanding the user's processing process, such as emergency treatment, normal treatment, etc., and subsequently simulating nursing content; using existing simulation technology and modeling technology for setting; and subsequently adaptively adjusting the model according to patient information.

[0030] Exemplarily, the training simulation process is: The user enters the simulation interface, which is divided into catheterization simulation mode and nursing simulation mode; the corresponding training simulation is carried out according to the simulation mode selected by the user; Catheterization simulation: The user performs PICC insertion operation according to the provided PICC standard range, presets various patient physiological backgrounds that may be encountered, such as physiological parameters exceeding the standard, normal and other background conditions, randomly generates corresponding patient physiological backgrounds during the simulation operation, identifies the insertion operation parameters and the treatment measures under the corresponding patient physiological backgrounds; Nursing Simulation Mode: Statistics are collected on various nursing situations that may be encountered, and corresponding nursing backgrounds are set. Users perform nursing operations under the corresponding nursing backgrounds and record corresponding nursing data.

[0031] In one embodiment, the training content corresponds to PICC placement training, and the method for the simulation unit to perform training simulation includes: Obtain various physiological parameters that may occur during the treatment process of the patient, and then the platform sets the background of each physiological parameter. Different physiological parameter backgrounds represent different changes in physiological parameters; and set qualified processing operations under each physiological parameter background; A catheterization training model for PICC catheterization simulation is established. The catheterization training model is a three-dimensional model built based on the existing visual modeling technology. It is equipped with various equipment used in the PICC catheterization process and can be adaptively adjusted according to patient information, such as shape, size and other related data.

[0032] When the user is training, the PICC standard range of the target user is obtained and displayed to the user; each physiological parameter background is randomly applied; the user simulates through the catheterization training model under the corresponding physiological parameter background, and records the corresponding simulation training data.

[0033] In one embodiment, the training results are evaluated according to the simulated training data in the training process. The evaluation can be based on existing evaluation technology, that is, whether the catheterization operation, nursing, etc. meet the preset standards. If the requirements are met, the training is considered qualified, otherwise the training is unqualified. For example, in the previous embodiment, the simulated training data is compared with the corresponding merge processing operation to determine whether the preset standards are met.

[0034] In one embodiment, the method for checking the user's treatment rights includes: Obtain user information, which mainly includes relevant information on whether the user has treatment authority. If the user is a medical staff of the target patient, the user information is marked with a treatment authority label. If the user is not a medical staff of the target patient, the user information includes the corresponding training result data.

[0035] Determine whether the user has a treatment authority label according to the user information; When the user has the treatment authority tag, the assessment authority check is passed; When the user does not have a treatment authority label, the user's training results for the target patient are identified from the user information. When the training result is qualified, the assessment authority check is passed; when the training result is unqualified, the assessment authority check is failed.

[0036] The prevention analysis module is used for the user to perform complication prevention analysis when performing treatment operations on target patients, and collect the user's treatment operation data in real time, such as collecting physiological parameters (such as heart rate, blood pressure, blood oxygen saturation, etc.) and operation process parameters (such as catheter insertion depth, angle, etc.) and complication occurrence (such as thrombosis, infection, etc.) during PICC catheterization in real time through sensors and imaging equipment; analyze the treatment operation data to determine whether the user's treatment operation meets the requirements, such as judging based on existing technologies, judging based on the judgment technology in the training unit, etc., to analyze whether the treatment operation meets the requirements and obtain the operation analysis results, which include qualified operation analysis and unqualified operation analysis; Carry out corresponding preventive treatment according to the obtained operation analysis results; if the operation analysis is qualified, no corresponding treatment will be carried out; if the operation analysis is unqualified, early warning intervention will be carried out to prompt the operation problem and display the correct treatment operation method, thereby reducing the incidence of PICC complications; according to the actual situation, preset treatment measures for different situations, and carry out matching treatment later.

[0037] In one embodiment, the method for analyzing the treatment operation data is: Establish potential analysis models based on machine learning, deep learning technologies, etc. to identify potential risks of complications; establish corresponding training data manually for training.

[0038] For example, physiological parameters (such as heart rate, blood pressure, blood oxygen saturation, etc.), operation process parameters (such as catheter insertion depth, angle, etc.), and complication conditions (such as thrombosis, infection, etc.) during PICC catheterization are collected in real time from sensors and imaging devices. The collected data is preprocessed by cleaning, denoising, and standardization to ensure the quality and consistency of the data. Features related to the PICC catheterization process and complication risk are extracted from the preprocessed data. The feature selection algorithm is used to screen out features that contribute significantly to the model's prediction performance to reduce the model's complexity and overfitting risk. Based on the selected features, appropriate machine learning or deep learning algorithms (such as logistic regression, support vector machine, random forest, neural network, etc.) are selected to build potential analysis models. The model is trained using historical data (including data sets with known complications) and model parameters are adjusted to optimize prediction performance. The model is validated using methods such as cross-validation and holdout to evaluate the model's prediction accuracy and generalization ability. The model is optimized based on the validation results, such as adjusting the model structure, adding regularization terms, and using ensemble learning.

[0039] The treatment operation data is analyzed through the potential analysis model to identify potential complication risks, which are integrated into complication analysis data. The operation analysis results are determined based on the complication analysis data. That is, if there is no complication risk, the operation analysis is qualified, otherwise the operation analysis is unqualified.

[0040] For example, historical data of 1,000 patients with PICC catheterization were obtained from the hospital's information system, including physiological parameters, operation process parameters, and complication occurrence; the data were cleaned, missing values ​​and outliers were removed, and standardized; features such as heart rate, blood pressure, blood oxygen saturation, catheter insertion depth, and angle were extracted from the data; features that significantly contributed to the prediction of complication risk were screened out using methods such as chi-square test and recursive feature elimination; a support vector machine algorithm was selected to construct a potential analysis model; 80% of the data was used to train the model and 20% of the data was used for verification; the prediction performance of the model was optimized by adjusting the kernel function and penalty parameters of the support vector machine; and the trained model was deployed.

[0041] In one embodiment, in order to improve the treatment operation effect, a corresponding operation tutorial can be generated according to the PICC standard range of the target patient, such as a preset initialization operation tutorial, and then adaptive parameter adjustment is performed according to the initial operation tutorial of the PICC standard range of the target patient to form an operation tutorial for the target patient. Specifically, there are multiple ways to set the operation tutorial for the target patient; The corresponding treatment steps are determined in real time based on the treatment operation data. For example, if a catheter is being placed, corresponding operation guidance is provided based on the identified treatment steps and operation tutorials, that is, the operation tutorials for the corresponding treatment steps are played to provide operation guidance.

[0042] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0043] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A PICC complication prevention training data intelligent processing system, characterized in that: It includes patient analysis module, training module and prevention analysis module; The patient analysis module is used to analyze patient information and determine the PICC standard range of the target patient, wherein the PICC standard range includes a physiological parameter standard range and an operation parameter standard range; The training module is used to simulate the PICC catheterization operation on the target patient, and includes a simulation unit and an authority unit; The simulation unit is used for the user to perform training simulation according to the preset training content, and evaluate the corresponding training results according to the simulated training data recorded during the training process, and the training results include qualified training and unqualified training; The authority unit is used to check the user's treatment authority and obtain the user's authority check result, wherein the authority check result includes authority check pass and authority check fail; When the authority check fails, a warning of restricted treatment will be issued; When the authority check is passed, no corresponding warning operation is performed; The prevention analysis module is used for the user to perform complication prevention analysis when performing treatment operations on target patients, collect the user's treatment operation data in real time, analyze the treatment operation data, and obtain operation analysis results, which include qualified operation analysis and unqualified operation analysis; Perform corresponding preventive measures based on the operation analysis results.

2. The PICC complication prevention training data intelligent processing system according to claim 1, characterized in that: Methods for determining the standard range of PICCs based on patient information include: Acquire the PICC medical data corresponding to the target patient during treatment in the hospital, perform standard judgment on the PICC medical data, and obtain the standard judgment result of the PICC medical data, wherein the standard judgment result includes compliance with standard requirements and non-compliance with standard requirements; Marking the PICC medical data that meets the standard requirements as standard material data, and marking the PICC medical data that does not meet the standard requirements as abnormal material data; Identify the initial physiological parameter range and the initial operating parameter range corresponding to the standard material data; identify the abnormal physiological parameter range and the abnormal operating parameter range corresponding to the abnormal material data; According to the abnormal physiological parameter range and the abnormal operating parameter range, the initial physiological parameter range and the initial operating parameter range are adjusted respectively to obtain the PICC standard range of the target patient; The standard optimization data of the target patient is acquired in real time, and the PICC standard range is adjusted in real time according to the standard optimization data.

3. The PICC complication prevention training data intelligent processing system according to claim 2, characterized in that: The methods for standard judgment of PICC medical data include: A treatment judgment model is established, and the expression of the treatment judgment model is: ; Where: (S, K) is the input data, S is the PICC medical data; K is the standard requirement, and the standard requirement is no complications; S→K means that the corresponding PICC medical data meets the standard requirements; the output data is the treatment judgment value LP(S, K), and the treatment judgment value is 1 or 0; Analyzing the PICC medical data by using the treatment judgment model to obtain a treatment judgment value corresponding to the PICC medical data; When the treatment judgment value is 1, the standard judgment result of the PICC medical data is in compliance with the standard requirements; When the treatment judgment value is 0, the standard judgment result of the PICC medical data is that it does not meet the standard requirements.

4. The PICC complication prevention training data intelligent processing system according to claim 1, characterized in that: Methods for determining the standard range of PICCs based on patient information include: Preset each patient category and the corresponding PICC standard range for each patient category; Matching is performed according to the patient information of the target patient, and marking the matched PICC standard range as the PICC initial range; The PICC initial range is displayed to the medical staff of the target patient, and the medical staff adjusts the PICC initial range to obtain the PICC standard range used by the target patient.

5. The PICC complication prevention training data intelligent processing system according to claim 1, characterized in that: The training content is PICC catheterization training. The training simulation methods of the simulation unit include: The platform presets various physiological parameter backgrounds and qualified processing operations under various physiological parameter backgrounds; establishes a catheterization training model for PICC catheterization simulation; When the user is training, the PICC standard range of the target user is obtained and displayed to the user; the physiological parameter background is randomly applied; the user simulates through the catheterization training model under the physiological parameter background, and the corresponding simulation training data is recorded.

6. The PICC complication prevention training data intelligent processing system according to claim 1, characterized in that: Methods for checking the user's treatment rights include: Acquire user information, and determine whether the user has a treatment authority tag according to the user information; When the user has a treatment authority tag, evaluating whether the user authority verification has passed; When the user does not have a treatment authority label, the training result of the user on the target patient is identified from the user information, and when the training result is qualified, the user authority verification is evaluated as passed; when the training result is unqualified, the user authority verification is evaluated as failed.

7. The PICC complication prevention training data intelligent processing system according to claim 1, characterized in that: Before the user performs treatment operations on the target patient, a corresponding operation tutorial is generated according to the target patient's PICC standard range; When the user performs a treatment operation on a target patient, the corresponding treatment steps are identified in real time according to the treatment operation data, and corresponding operation guidance is provided according to the treatment steps and operation tutorials.

8. The PICC complication prevention training data intelligent processing system according to claim 1, characterized in that: Methods for analyzing treatment operation data include: Establishing a potential analysis model; analyzing the treatment operation data through the potential analysis model to obtain complication analysis data, and determining the operation analysis result according to the complication analysis data.

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