Needle insertion level quantitative standard and device based on needle insertion process of nurse

Abstract

The invention provides an acupuncture level quantification standard and device based on the acupuncture process of a nurse, and aims to establish an objective and scientific evaluation system by performing quantitative analysis on key data of the acupuncture process so as to improve the nursing quality. The method comprises three main steps of data collection and preprocessing, evaluation standard establishment and acupuncture process scoring. Acceleration data in the acupuncture process are collected in real time through a three-axis accelerometer, a speed and displacement sequence is obtained through filtering and integral calculation, the operation process is divided into three stages of skin contact, subcutaneous insertion and target depth approaching, and statistical characteristics of all the stages are extracted to serve as quantitative indexes. The evaluation standard covers key indexes such as speed stability, sudden stop times and insertion time, and quantitative evaluation of the needle insertion level of the nurse is achieved in combination with the segmentation scoring weight and the comprehensive scoring rule. The device consists of a hardware module (a sensor, a microprocessor, a wireless communication module and the like) and a software module (a data processing and analysis algorithm), supports real-time data transmission and feedback, and has the characteristics of portability, automation and high precision. Scientific basis is provided for continuous improvement of the nursing level, and more comfortable and safer medical experience is created for a patient.

Description

Technical Field

[0001] The present disclosure relates to the field of nursing and the technical field of nursing medical equipment, and in particular to a quantitative standard and device for acupuncture level based on a nurse's acupuncture process. Background Art

[0002] Acupuncture, especially intravenous injection, is one of the indispensable core operations in clinical nursing. In medical behaviors such as infusion, blood collection, and drug injection, the accuracy and efficiency of venipuncture directly affect the patient's treatment effect and medical experience. Although this technology is a basic skill in nursing, its operation difficulty is significantly increased due to the patient's physiological characteristics, vascular conditions, and the experience and technical level of the nurse.

[0003] Individual differences among patients are an important factor affecting the success rate of acupuncture. Patients of different ages, body shapes, and health conditions have significant differences in the thickness, depth, and distribution of their blood vessels. For example, children and adolescents have small blood vessels and thin walls, which can easily lead to vascular damage due to improper operation; and obese patients have thicker subcutaneous fat, which increases the difficulty of locating blood vessels. In addition, changes in the condition or psychological state may also affect the filling of blood vessels, further increasing the challenge of puncture. These factors, combined with differences in nurses' skill levels, increase the risk of acupuncture failure and easily cause psychological discomfort to patients.

[0004] However, when evaluating nurses' acupuncture skills, they have long relied on subjective feedback from patients. This method is not only difficult to avoid the bias of patients' subjective feelings such as pain and tension, but also may affect the objectivity of the evaluation due to the relationship between nurses and patients. For example, some patients may conceal their true feelings due to fear of medical authority or out of politeness, while other patients may give overly harsh evaluations due to dissatisfaction with nursing services. This evaluation model that lacks scientific quantitative standards not only cannot accurately reflect the true technical level of nurses, but also cannot provide an effective basis for improving nursing levels.

[0005] Based on the above problems, the present invention aims to scientifically evaluate the nurses' operating process through objective data, provide directions for technical improvements for individual nurses, and provide reliable quantitative standards for nursing management of departments and hospitals, thereby helping to improve the overall nursing service level and improve patients' medical experience. Summary of the invention

[0006] The purpose of the present invention is to provide a quantitative standard and device for acupuncture level based on the acupuncture process of nurses.

[0007] The technical solution to achieve the purpose of the present invention is: a quantitative standard of acupuncture level based on the acupuncture process of nurses, characterized by comprising the following steps:

[0008] Step S1: data collection and preprocessing;

[0009] Step S2: Establishing evaluation criteria and scoring of the acupuncture process;

[0010] Step S3: Rating feedback and improvement suggestions.

[0011] Preferably, the step S1 includes the following steps:

[0012] Step S11: data collection, using a three-axis accelerometer to collect three-dimensional acceleration data during the needle insertion process, ensuring that the collection frequency is not less than 2 Hz;

[0013] Step S12: signal filtering, using the Kalman filter method to remove noise in the acceleration data, and remove high-frequency noise caused by hand tremor or environmental interference;

[0014] Step S13: Velocity and displacement calculation, time integration of acceleration data to obtain velocity ,in Representative time, represents one of the three directions of the three-axis accelerometer, , Representative Moment The acceleration data collected in each direction, represents the sampling time, and further integration gives the displacement , and then form the instantaneous speed and displacement sequence during the needle insertion process as the basic data for the evaluation standard;

[0015] Step S14: Segmentation and feature extraction, dividing the needle insertion process into the following three stages: skin contact stage, subcutaneous insertion stage, and approaching target depth stage, and extracting the statistical characteristics of displacement, velocity and acceleration of each segment respectively.

[0016] Preferably, the three stages are defined by the following table:

[0017] stage definition Skin contact stage From the beginning of acupuncture to the moment when the needle tip just touches the skin Subcutaneous insertion stage The skin contact stage ends when the needle tip completely enters the skin layer but has not yet reached the target depth. Approaching target depth stage From the end of the subcutaneous insertion phase to the phase where the needle tip gradually approaches the ideal depth

[0018] Preferably, the statistical features in step S14 include mean value, standard deviation and coefficient of variation.

[0019] Preferably, the step S2 comprises the following steps:

[0020] Step S21: Determine the evaluation indexes, including speed stability, number of emergency stops, insertion time, and stability. Their definitions and evaluation methods are shown in the following table, where the standard deviation , , represents the sampling time, represents the total sampling time of the velocity time series, is the mean of the speed time series; the coefficient of variation , , , , represents the total sampling time of the displacement time series, is the displacement time series mean:

[0021] Evaluation indicators definition Evaluation Method Specific values Speed ​​stability Reflects the stability of the speed during needle insertion Quantification by calculating the standard deviation of the velocity time series Less than 0.1m / s: stable (10 points) Between 0.1m / s and 0.3m / s: relatively stable (7 points) Between 0.3m / s and 0.5m / s: unstable (5 points) Greater than 0.5m / s: seriously unstable (2 points) Emergency stop times The number of times the needle tip speed suddenly drops to near zero during the insertion process Count the emergency stop events that meet the conditions in the speed sequence, that is, the instantaneous speed is less than 0.2m / s Number of emergency stops = 0: no points deducted Number of emergency stops = 1: 2 points deducted Number of emergency stops ≥ 2: 3 points deducted for each emergency stop Insert time Total time from skin contact to reaching target depth Calculate the total time through the displacement sequence and set the scoring interval Total time less than 2s: fast (10 points) Total time between 2s and 5s: always (7 points) Total time greater than 5s: too slow (5 points) Stability Reflects the continuity and regularity of the needle insertion process Calculate the coefficient of variation of a displacement sequence Coefficient of variation less than 0.1: high stability (10 points) Coefficient of variation between 0.1 and 0.3: medium stability (7 points) Coefficient of variation greater than 0.3: low stability (5 points)

[0022] Step S22: Segmented scoring criteria, based on the three stages of needle insertion, namely, the skin contact stage, the subcutaneous insertion stage, and the target depth approach stage, the scoring criteria are determined as follows:

[0023] stage index Scoring Rules Skin contact calculation Displacement stability Calculate the coefficient of variation and score the stability according to step S21 Speed ​​Change Calculate the maximum rate of change of speed Subcutaneous insertion Speed ​​stability According to the speed stability score in step S21 Emergency stop times According to the deduction score of the number of emergency stops in step S21 Approaching the target Insert time Insert the time score according to step S21 Stability According to the stability score in step S21

[0024] Step S23: benchmark pattern and similarity scoring, considering the differences in blood vessel length and thickness among different patients, using a dynamic benchmark pattern, including dynamic benchmark construction and actual data comparison;

[0025] Step S24: abnormal detection and deduction mechanism, detect acceleration sequence changes to meet For the conditional points, 3 points will be deducted for each abnormal event;

[0026] Step S25: weighted comprehensive score, based on the evaluation index of step S21, the segmented scoring standard of step S22, the similarity score of step S23, and the deduction mechanism of step S24 to calculate the weighted comprehensive score ,in is the number of indicators, For the The weight of the step indicator, Sure, Indicates The importance score of the item is obtained by experts or through historical data statistics. For the The score of each indicator.

[0027] Preferably, the dynamic benchmark construction process of step S23 is:

[0028] Step S231: Collection of high-quality operation data. In clinical practice, process data of acupuncture operations completed by experienced nurses are collected to ensure that the operation is technically close to the optimal level. Through the step S1, the data is preprocessed, key features are extracted, and a high-quality operation sample library is formed; diversified samples are expanded, and operation data covering various vascular conditions and clinical scenarios are collected and organized for different patient groups (such as children, obese patients, and elderly patients); data clustering and feature induction, clustering algorithms (such as K-means clustering) are used to classify the collected operation sample data, and data with similar vascular conditions and operation characteristics are divided into the same group, and different age-stage benchmark modes for adolescents, adults, and the elderly are divided into thin, normal, and fat people at each age stage according to the BIM index. Therefore, a total of 9 categories are generated, and the feature mean and standard deviation of each category are extracted to form a group feature template. ,in Indicates Class operation mode;

[0029] Step S232: Data analysis and pattern extraction: construct corresponding standard feature ranges according to the statistical characteristics of different groups, calculate the mean value and double standard deviation range of the standard deviation based on high-quality samples, and use them as standard reference values ​​for speed stability. Combined with the frequency of emergency stop events of samples in the same group, set a reasonable deduction threshold;

[0030] Step S233: Dynamic update mechanism, continuously collect and add new operation data, evaluate whether the standard library needs to be updated through comparative analysis with existing data (such as feature similarity calculation); use machine learning methods (such as long short-term memory network based on time series, LSTM) to comprehensively analyze existing and new data, update operation templates, and ensure that the dynamic standard library is consistent with clinical practice; set special case standards for special patient types (such as abnormal vascular conditions);

[0031] Step S234: During the evaluation process, the individualized evaluation criteria are combined with the patient's vascular characteristics and scenario requirements to dynamically match the optimal template in the standard library to generate targeted evaluation criteria; the dynamic standard library is combined with the scoring system to adjust the weight or scoring threshold of the evaluation index in real time to ensure that the scoring results are more in line with the actual clinical situation, and at the same time, output improvement suggestions.

[0032] Preferably, the actual data comparison process in step S23 is to use the Pearson correlation coefficient to evaluate the similarity. The high and low ratings, is highly similar (10 points), is medium similar (score 7), The similarity is low (5 points).

[0033] Preferably, step S3 is score feedback and improvement suggestions. For nursing processes with low scores, specific improvement directions can be analyzed in combination with each score item.

[0034] The device for quantifying the standard of acupuncture level based on the acupuncture process of nurses is characterized in that it includes a hardware module and a software module, wherein the hardware module includes a three-axis accelerometer, a microprocessor (MCU), a wireless communication module, a built-in storage module, a power module, a shell and a fixed structure; the three-axis accelerometer collects three-dimensional acceleration data of hand movements during acupuncture in real time, and the sampling frequency is not less than 2Hz; the microprocessor (MCU) integrates a data preprocessing unit to filter and eliminate noise on the collected acceleration signal, and completes preliminary calculations, including time integration of speed and displacement; the wireless communication module supports Wi-Fi or Bluetooth function, and transmits the preprocessed data to the computing terminal; the built-in storage module provides a storage unit for temporarily storing collected data to avoid data loss; the power module includes a lithium battery and a power management system, which supports long-term stable operation of the equipment and can be recharged through USB; the shell and the fixed structure can be fixed on the nurse's gloves, needle handle or other operating parts; the software module includes Data processing and feature extraction module, scoring and evaluation module, anomaly detection module, data storage and upload module; the data processing and feature extraction module filters the acceleration data, calculates the speed, displacement and related statistical characteristics (mean value, standard deviation, coefficient of variation), divides the needle insertion process into different stages (the skin contact stage, the subcutaneous insertion stage, the target depth approach stage), and extracts the characteristic data of each stage; the scoring and evaluation module scores each indicator (the speed stability, the number of emergency stops, the insertion time, the stability) in sections according to the pre-set evaluation criteria, calculates the total score by weight and generates a comprehensive evaluation result of the acupuncture level; the anomaly detection module detects emergency stop events and abnormal acceleration in real time, and records the number and time points of the anomaly for deduction and further analysis; the data storage and upload module stores the scoring results and time series data in the device memory, supports storage classified by date, provides automatic or manual upload function, and sends the data to the computer through the wireless communication module.

[0035] Compared with the prior art, the present invention has the following significant advantages:

[0036] (1) The present invention is based on quantified kinematic data (such as time series data such as acceleration, velocity, displacement, etc.), and through a series of statistical features (such as mean, standard deviation, coefficient of variation, etc.) and mathematical models to comprehensively evaluate the technical level of the nurse's acupuncture process. Compared with the traditional evaluation method that relies on the patient's subjective feedback, the present invention provides a more objective and repeatable quantitative evaluation method, avoiding the evaluation bias caused by differences in the patient's subjective feelings or emotional factors.

[0037] (2) The level of nurses is evaluated from the overall process of acupuncture. The entire operation process is also subdivided into multiple key stages (such as contact with the skin, subcutaneous insertion, and approaching the target depth). The speed stability, operation consistency, abnormal events, etc. of different stages are analyzed respectively to construct segmented scoring rules. Compared with the single-dimensional evaluation of traditional evaluation methods, this invention can capture more detailed operation information and comprehensively reflect the technical characteristics of nurses and the links that need to be improved.

[0038] (3) The hardware device designed by the present invention is based on a three-axis accelerometer, which has the characteristics of small size and strong adaptability. It can be easily installed on the needle handle to collect dynamic data of nurses' operations in real time. In addition, the built-in algorithm can automatically preprocess, extract features and calculate scores for the collected data to generate quantitative evaluation results of acupuncture technology. This automated process greatly reduces the workload of manual recording and analysis, improves evaluation efficiency, and provides immediate feedback and improvement basis for nursing management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is the overall flow chart of the acupuncture level quantification standard of the present invention;

[0040] Figure 2 This is a flow chart of data collection and preprocessing of the present invention;

[0041] Figure 3 The establishment of the evaluation criteria of the present invention and the scoring flow chart of the acupuncture process;

[0042] Figure 4 The flowchart of establishing the dynamic standard library of the present invention;

[0043] Figure 5 This is a diagram showing the contents of the standard device for quantifying acupuncture levels according to the present invention. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0045] Example 1: Acupuncture level evaluation method based on nurse acupuncture process data

[0046] This embodiment aims to illustrate how to evaluate the nurse's operation level based on the acupuncture process data in actual operation using the acupuncture level quantitative standard proposed in this invention. Figure 1 The steps are to collect and analyze the data of the needle insertion process and quantify the evaluation indicators, and finally generate a comprehensive score to quantify the nurses' operating level and provide improvement suggestions.

[0047] First, Figure 2As shown, data collection and preprocessing are performed. When the nurse performs intravenous injection, the three-axis accelerometer installed on the needle handle is used to collect three-dimensional acceleration data of the needle insertion process in real time. To ensure the integrity and accuracy of the data, the sampling frequency is set to no less than 2Hz. The specific operation is as follows: Use a bandpass filter to preprocess the original acceleration signal to remove high-frequency noise and low-frequency drift interference. The specific filtering formula that can be used is:

[0048]

[0049] in, is the cut-off frequency, is the filter order, and the actual parameter is dynamically adjusted according to the data characteristics. Then the velocity calculation is performed, and the filtered acceleration data is time-integrated to calculate the instantaneous velocity and displacement sequence:

[0050]

[0051] The time integral adopts the trapezoidal integral method to reduce the calculation error. Then, segmentation processing and feature extraction are carried out. According to the actual operation of the acupuncture process, the needle insertion process is divided into three stages: contact with the skin, subcutaneous insertion, and approaching the target depth. The definition of each stage is shown in the following table:

[0052] stage definition Skin contact stage From the beginning of acupuncture to the moment when the needle tip just touches the skin Subcutaneous insertion stage The skin contact stage ends when the needle tip completely enters the skin layer but has not yet reached the target depth. Approaching target depth stage From the end of the subcutaneous insertion phase to the phase where the needle tip gradually approaches the ideal depth

[0053] At each stage, the extraction speed , displacement , acceleration The statistical characteristics of the velocity, including the mean value, standard deviation, and coefficient of variation of the displacement, and the mean value, standard deviation, and coefficient of variation of the acceleration, form a feature matrix.

[0054] Then, according to Figure 3 Conduct evaluation criteria and scoring. Construct the following evaluation criteria and scoring rules: ① Quantitative indicators and scoring criteria. Speed ​​stability: measure the smoothness of needle insertion through the speed standard deviation. The smaller the standard deviation, the higher the score. Number of emergency stops: record the sharp change points in the acceleration sequence, and deduct a fixed score for each emergency stop. Insertion time: calculate the score based on the deviation between the actual operation time and the reasonable range. Full marks will be awarded for operations within the reasonable range, and points will be deducted as appropriate if they exceed the range. Their scores and deductions are based on the following table:

[0055] Evaluation indicators definition Evaluation Method Specific values Speed ​​stability Reflects the stability of the speed during needle insertion Quantification by calculating the standard deviation of the velocity time series Less than 0.1m / s: stable (10 points) Between 0.1m / s and 0.3m / s: relatively stable (7 points) Between 0.3m / s and 0.5m / s: unstable (5 points) Greater than 0.5m / s: seriously unstable (2 points) Emergency stop times The number of times the needle tip speed suddenly drops to near zero during the insertion process Count the emergency stop events that meet the conditions in the speed sequence, that is, the instantaneous speed is less than 0.2m / s Emergency stop number = 0: no points deducted Emergency stop number = 1: 2 points deducted Emergency stop number ≥ 2: 3 points deducted for each emergency stop Insert time Total time from skin contact to reaching target depth Calculate the total time through the displacement sequence and set the scoring interval Total time less than 2s: fast (10 points) Total time between 2s and 5s: always (7 points) Total time greater than 5s: too slow (5 points) Stability Reflects the continuity and regularity of the needle insertion process Calculate the coefficient of variation of a displacement sequence Coefficient of variation less than 0.1: high stability (10 points) Coefficient of variation between 0.1 and 0.3: medium stability (7 points) Coefficient of variation greater than 0.3: low stability (5 points)

[0056] ② Segment scoring rules. The skin contact stage focuses on evaluating the speed change when contacting the skin, setting a reasonable threshold, and deducting points for the part exceeding the threshold. The subcutaneous insertion stage is scored based on the speed stability and average insertion speed of the insertion process. The target depth approach stage focuses on evaluating the stability of the operation speed and the deceleration control ability near the target depth. Their scoring criteria are:

[0057] stage index Scoring Rules Skin contact calculation Displacement stability Calculate the coefficient of variation and score the stability according to step S21 Speed ​​Change Calculate the maximum rate of change of speed Subcutaneous insertion Speed ​​stability According to the speed stability score in step S21 Emergency stop times According to the deduction score of the number of emergency stops in step S21 Approaching the target Insert time Insert the time score according to step S21 Stability According to the stability score in step S21

[0058] ③ Abnormal detection and deduction mechanism. By analyzing the acceleration and speed sequence, abnormal events (such as emergency stops, sudden speed increase) are detected. A certain number of points are deducted for each abnormality. The emergency stop threshold is defined as the instantaneous acceleration change exceeding ④ Weighted comprehensive score. The segment scores are combined into a total score according to the set weights:

[0059]

[0060] in, They are the scoring weights for the three stages, and the weights are set to 0.3, 0.4, and 0.3 according to the actual operation importance.

[0061] Finally, actual application and result output. After the operation is completed, the device transmits the collected characteristic data and scoring results to the computer via wireless, and automatically generates a report, including segmented scores, abnormal event statistics, total scores, and improvement suggestions. Nurses can intuitively understand their operating level and the aspects that need improvement based on the scoring results, providing a basis for subsequent training and quality improvement.

[0062] This embodiment achieves objective measurement of nurses' acupuncture skills through quantifiable evaluation of actual operations, overcomes the shortcomings of traditional subjective evaluation methods, and provides a scientific basis for improving nursing quality.

[0063] Example 2: Method for establishing a dynamic standard library

[0064] This embodiment is intended to illustrate how to use the data of the nurse's acupuncture process to Figure 4 Build and continuously optimize the dynamic standard library to provide a more comprehensive and accurate reference for the quantitative evaluation of acupuncture level. By dynamically updating the standard library, we can adapt to the individual characteristics and operation scenarios of different patients and ensure the applicability and scientificity of the evaluation method.

[0065] The data sources of the dynamic standard library include the following two categories: ① Collection of high-quality operation data, which collects process data of acupuncture operations completed by experienced nurses in clinical practice to ensure that the operation is technically close to the optimal level. Figure 1Step S1 in the above process preprocesses the data, extracts key features, and forms a high-quality operation sample library. ② Expansion of diversified samples: for different patient groups (such as children, obese patients, and elderly patients), collect and organize operation data covering various vascular conditions and clinical scenarios to ensure that the standard library is diverse and representative.

[0066] Use clustering algorithms (such as K-means clustering) to classify the collected operation sample data, divide the data with similar vascular conditions and operation characteristics into the same group, extract the characteristic mean and standard deviation of each group, and form a group characteristic template:

[0067]

[0068] in, Representative According to the statistical characteristics of different groups, the corresponding standard feature range is constructed for setting the threshold of the evaluation index. For example, the mean value and double standard deviation range of the standard deviation are calculated based on the high-quality samples as the standard reference value of speed stability. Combined with the frequency of emergency stop events of samples in the same group, a reasonable deduction threshold is set.

[0069] Continuously collect and add new operation data, and evaluate whether the standard library needs to be updated by comparing and analyzing it with existing data (such as feature similarity calculation). Use machine learning methods (such as long short-term memory networks based on time series, LSTM) to conduct comprehensive analysis of existing and new data, update operation templates, and ensure that the dynamic standard library is consistent with clinical practice. Set special case standards for special patient types (such as abnormal vascular conditions) to avoid "one-size-fits-all" evaluations that affect the overall evaluation effect.

[0070] During the evaluation process, the optimal template in the standard library is dynamically matched to generate targeted evaluation criteria in combination with the patient's vascular characteristics and scenario requirements. For example, for pediatric patients, a template with low acceleration and low number of emergency stops is selected as a comparison basis; for obese patients, the displacement characteristics and the accuracy of depth control are emphasized. The dynamic standard library is combined with the scoring system to adjust the weight or scoring threshold of the evaluation indicators in real time to ensure that the scoring results are more in line with the actual clinical situation. At the same time, improvement suggestions are output, such as the shortcomings of a specific nurse in a certain type of patient.

[0071] Through the establishment of a dynamic standard library, the evaluation system can adapt to different operation scenarios and patient characteristics, realize the personalization and dynamicization of evaluation standards, and significantly improve the accuracy and clinical applicability of quantitative evaluation. In addition, the continuous optimization mechanism of the standard library ensures that the evaluation method can maintain long-term consistency with clinical practice, providing a reliable guarantee for the scientific evaluation and continuous improvement of nursing level.

[0072] Example 3: Operation method and data flow of a nurse using a needle level quantitative standard device

[0073] This embodiment aims to illustrate how nurses use the acupuncture level quantitative standard device of the present invention to perform acupuncture operations, and at the same time describes in detail the processing and flow of data inside the device, intuitively reflecting the working principle of the device and its actual role in quantitative evaluation.

[0074] First, install and initialize the device. Install the needle level quantitative standard device on the needle handle. The device adopts an adjustable clip design to ensure a firm fit with needle handles of different models without affecting the nurse's operating feel and patient comfort. After starting the device, the device automatically calibrates the three-axis accelerometer to ensure that the initial state of the data is zero bias and excludes non-operational actions, such as interference signals during the device fixing process. Then, normal needle operation can be performed.

[0075] When the nurse performs the acupuncture operation, the device collects three-dimensional acceleration data in real time and records the dynamic changes during the operation at a frequency of not less than 2Hz. The data collection starts at the moment when the needle tip touches the skin, and ends when the needle tip stabilizes at the target position. The microprocessor in the device performs preliminary processing on the collected acceleration data through the following process: a low-pass filter is used to filter out high-frequency noise, and continuous acceleration data is sampled at intervals. The processed data is transmitted to an external terminal, such as a connected computer, through a wireless communication module, Bluetooth or Wi-Fi. Encryption protocols, such as AES, are used during data transmission to protect patient privacy and data security.

[0076] After the data is collected, the acupuncture process is segmented according to step S14, and the different stages of skin contact, subcutaneous insertion and target depth stability are automatically identified, and the corresponding statistical characteristics of acceleration, velocity and displacement are extracted. According to the basic information of the patient, such as age, body shape and operation data characteristics, the system matches the closest reference template from the dynamic standard library and scores the nurse's operation according to the template. The system scores the speed stability, number of emergency stops, insertion time and other indicators in the operation according to the scoring criteria of step S2. If the operation is abnormal (such as the number of emergency stops exceeds the standard), the system will record detailed data and make suggestions for improvement.

[0077] This example demonstrates the efficiency and operability of the acupuncture level quantitative standard device by describing the specific usage method and data flow of the device. The device can not only provide nurses with scientific operation quality evaluation, but also help to continuously improve the quality of nursing through long-term data accumulation, and provide patients with better medical services.

[0078] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0079] Although the above describes the specific implementation methods of the present invention, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A quantitative standard for acupuncture level based on the acupuncture process of nurses, characterized by: The steps include: Step S1: data collection and preprocessing; Step S2: Establishing evaluation criteria and scoring of the acupuncture process; Step S3: Rating feedback and improvement suggestions.

2. The quantitative standard of acupuncture level based on the acupuncture process of nurses according to claim 1 is characterized in that: The step S1 includes the following steps: Step S11: data collection, using a three-axis accelerometer to collect three-dimensional acceleration data during the needle insertion process, ensuring that the collection frequency is not less than 2 Hz; Step S12: signal filtering, using the Kalman filter method to remove noise in the acceleration data, and remove high-frequency noise caused by hand tremor or environmental interference; Step S13: Velocity and displacement calculation, time integration of acceleration data to obtain velocity ,in Representative time, represents one of the three directions of the three-axis accelerometer, , Representative Moment The acceleration data collected in each direction, represents the sampling time, and further integration gives the displacement , and then form the instantaneous speed and displacement sequence during the needle insertion process as the basic data for the evaluation standard; Step S14: Segmentation and feature extraction, dividing the needle insertion process into the following three stages: skin contact stage, subcutaneous insertion stage, and approaching target depth stage, and extracting the statistical characteristics of displacement, velocity and acceleration of each segment respectively.

3. The quantitative standard of acupuncture level based on the acupuncture process of nurses according to claim 2 is characterized in that: The 3 phases are defined by the following table:

4. The quantitative standard of acupuncture level based on the acupuncture process of nurses according to claim 2, characterized in that: The statistical features in step S14 include mean value, standard deviation and coefficient of variation.

5. The acupuncture level quantification standard based on the acupuncture process of nurses according to claim 1 is characterized in that: The step S2 includes the following steps: Step S21: Determine the evaluation indexes, including speed stability, number of emergency stops, insertion time, and stability. Their definitions and evaluation methods are shown in the following table, where the standard deviation , , represents the sampling time, represents the total sampling time of the velocity time series, is the mean of the speed time series; the coefficient of variation , , , , represents the total sampling time of the displacement time series, is the displacement time series mean: Step S22: Segmented scoring criteria, based on the three stages of needle insertion, namely, the skin contact stage, the subcutaneous insertion stage, and the target depth approach stage, the scoring criteria are determined as follows: Step S23: benchmark pattern and similarity scoring, considering the differences in blood vessel length and thickness among different patients, using a dynamic benchmark pattern, including dynamic benchmark construction and actual data comparison; Step S24: abnormal detection and deduction mechanism, detect acceleration sequence changes to meet For the conditional points, 3 points will be deducted for each abnormal event; Step S25: weighted comprehensive score, based on the evaluation index of step S21, the segmented scoring standard of step S22, the similarity score of step S23, and the deduction mechanism of step S24 to calculate the weighted comprehensive score ,in is the number of indicators, For the The weight of the step indicator, Sure, Indicates The importance score of the item is obtained by experts or through historical data statistics. For the The score of each indicator.

6. The quantitative standard of acupuncture level based on the acupuncture process of nurses according to claim 1, characterized in that: The dynamic benchmark construction process of step S23 is as follows: Step S231: Collection of high-quality operation data. In clinical practice, process data of acupuncture operations completed by experienced nurses are collected to ensure that the operation is technically close to the optimal level. Through the step S1, the data is preprocessed, key features are extracted, and a high-quality operation sample library is formed; diversified samples are expanded, and operation data covering various vascular conditions and clinical scenarios are collected and organized for different patient groups (such as children, obese patients, and elderly patients); data clustering and feature induction, clustering algorithms (such as K-means clustering) are used to classify the collected operation sample data, and data with similar vascular conditions and operation characteristics are divided into the same group, and different age-stage benchmark modes for adolescents, adults, and the elderly are divided into thin, normal, and fat people at each age stage according to the BIM index. Therefore, a total of 9 categories are generated, and the feature mean and standard deviation of each category are extracted to form a group feature template. ,in Indicates Class operation mode; Step S232: Data analysis and pattern extraction: construct corresponding standard feature ranges according to the statistical characteristics of different groups, calculate the mean value and double standard deviation range of the standard deviation based on high-quality samples, and use them as standard reference values ​​for speed stability. Combined with the frequency of emergency stop events of samples in the same group, set a reasonable deduction threshold; Step S233: Dynamic update mechanism, continuously collect and add new operation data, evaluate whether the standard library needs to be updated through comparative analysis with existing data (such as feature similarity calculation); use machine learning methods (such as long short-term memory network based on time series, LSTM) to comprehensively analyze existing and new data, update operation templates, and ensure that the dynamic standard library is consistent with clinical practice; set special case standards for special patient types (such as abnormal vascular conditions); Step S234: During the evaluation process, the individualized evaluation criteria are combined with the patient's vascular characteristics and scenario requirements to dynamically match the optimal template in the standard library to generate targeted evaluation criteria; the dynamic standard library is combined with the scoring system to adjust the weight or scoring threshold of the evaluation index in real time to ensure that the scoring results are more in line with the actual clinical situation, and at the same time, output improvement suggestions.

7. The acupuncture level quantification standard based on the acupuncture process of nurses according to claim 1, characterized in that: The actual data comparison process of step S23 is to use the Pearson correlation coefficient to evaluate the similarity. The high and low ratings, is highly similar (10 points), is medium similar (score 7), The similarity is low (5 points).

8. The acupuncture level quantification standard based on the acupuncture process of nurses according to claim 1, characterized in that: The step S3 is score feedback and improvement suggestions. For nursing processes with low scores, specific improvement directions can be analyzed in combination with each score item.

9. A quantitative standard device for acupuncture level based on the acupuncture process of nurses, characterized in that: The device comprises a hardware module and a software module, wherein the hardware module comprises a three-axis accelerometer, a microprocessor (MCU), a wireless communication module, a built-in storage module, a power module, a housing and a fixed structure; the three-axis accelerometer collects three-dimensional acceleration data of hand movements during acupuncture in real time, with a sampling frequency of not less than 2 Hz; the microprocessor (MCU) integrates a data preprocessing unit to filter and eliminate noise on the collected acceleration signal, and completes preliminary calculations, including time integration of velocity and displacement; the wireless communication module supports Wi-Fi or Bluetooth functions, and transmits the preprocessed data to a computing terminal; The built-in storage module provides a storage unit for temporarily storing collected data to avoid data loss; the power module includes a lithium battery and a power management system to support long-term stable operation of the device and can be recharged via USB; the housing and the fixing structure can be fixed on the nurse's gloves, needle handle or other operating parts; the software module includes a data processing and feature extraction module, a scoring and evaluation module, an abnormality detection module, and a data storage and upload module; the data processing and feature extraction module filters the acceleration data, and calculates the speed, displacement and related statistical characteristics (mean value, standard deviation, coefficient of variation), and divides the needle insertion process into different stages (the skin contact stage, the subcutaneous insertion stage, the subcutaneous insertion stage, the skin contact ... stage, the approaching target depth stage), extracting characteristic data of each stage; the scoring and evaluation module scores each indicator (the speed stability, the number of emergency stops, the insertion time, and the stability) in sections according to the pre-set evaluation criteria, calculates the total score by weight and generates a comprehensive evaluation result of the acupuncture level; the abnormality detection module detects emergency stop events and abnormal acceleration in real time, and records the number and time of abnormal occurrence for deduction and further analysis; the data storage and upload module stores the scoring results and time series data in the device memory, supports storage by date classification, provides automatic or manual upload function, and sends the data to the computer through the wireless communication module.

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