Intelligent control method and system for a bone trauma treatment instrument

Abstract

The present invention discloses an intelligent control method and system for a bone trauma treatment instrument, specifically relating to the technical field of intelligent medical devices; based on the patient data set stored inside the treatment instrument, patients undergoing bone trauma treatment are screened and classified to obtain the group with frequent adjustment of treatment parameters. The temporal changes of the treatment plan for this group within the treatment cycle are analyzed to evaluate the internal matching degree of the treatment plan for bone tissue repair. At the same time, the real-time feedback data of this group under the stimulation of the same type of treatment parameters is processed to quantify the divergence degree of the group response and judge the acceptance stability of this group for the treatment plan. By analyzing the complexity of the sensing signals collected during the treatment process, the necessity of enhancing the diversified regulation strategy is evaluated. Considering the above multiple analysis results comprehensively, the parameter configuration strategy of the bone trauma treatment instrument is dynamically adjusted to ensure that the treatment instrument can be widely applied to the field of intelligent bone trauma treatment and has significant clinical value.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent medical devices, and more specifically, to an intelligent control method and system for a bone trauma treatment instrument. Background Art

[0002] Traditional bone trauma treatment instruments usually adopt fixed treatment parameters and implement the same treatment plan for different patients. However, due to individual differences among patients in terms of age, gender, bone density, trauma type, and healing ability, the fixed treatment parameters are difficult to meet the needs of all patients, which may lead to poor treatment effects or extended recovery periods. Some patients need to frequently adjust the treatment parameters during the treatment process to seek a treatment plan more suitable for their own conditions. In addition, the existing treatment instruments lack the ability to analyze the real-time feedback data of patients and cannot dynamically adjust the treatment parameters according to the individual responses of patients, resulting in a lack of precision and personalization in the treatment process.

[0003] To solve the above problems, a technical solution is provided as follows. Summary of the Invention

[0004] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an intelligent control method and system for a bone trauma treatment instrument to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent control method for a bone trauma treatment instrument includes the following steps:

[0007] Based on the patient dataset stored inside the bone trauma treatment instrument, screen and classify the patients receiving bone trauma treatment to obtain the group with frequent treatment parameter adjustments;

[0008] Analyze the temporal changes of the treatment plan of the group with frequent treatment parameter adjustments during the treatment cycle to evaluate the internal matching degree of the treatment plan for bone tissue repair;

[0009] Analyze the real-time feedback data during the treatment process of the group with frequent treatment parameter adjustments to evaluate the response divergence degree of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters;

[0010] Based on the internal matching degree of the treatment plan for bone tissue repair and the response divergence degree of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters, judge the acceptance stability of the group with frequent treatment parameter adjustments for the treatment plan;

[0011] When the acceptance stability of the treatment plan for the group with frequent treatment parameter adjustments is low acceptance stability, by analyzing the complexity of the sensing signals collected by the bone trauma treatment instrument during the treatment process, evaluate the necessity of the bone trauma treatment instrument to enhance the diversified regulation strategy;

[0012] Comprehensively analyze the internal matching degree of the treatment plan for bone tissue repair, the response divergence of the group with frequent treatment parameter adjustments under the stimulation of similar treatment parameters, and the necessity of the bone trauma treatment instrument to enhance the diversified regulation strategy, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument.

[0013] In a preferred embodiment, based on the patient data set stored in the bone trauma treatment instrument, screen and classify the patients receiving bone trauma treatment to obtain the group with frequent treatment parameter adjustments, specifically:

[0014] Extract the treatment data of all patients from the database of the bone trauma treatment instrument;

[0015] Clean the extracted data to exclude duplicate, invalid or abnormal data;

[0016] Based on the patient treatment records and the parameter change frequency, design a screening rule to screen the group of patients with frequent treatment parameter adjustments during the treatment process: for each patient, for each treatment parameter, calculate the adjustment frequency of the parameter during the entire treatment cycle, and the adjustment frequency is defined as the number of adjustments of the parameter divided by the total duration of the treatment cycle; according to the adjustment frequencies of all treatment parameters, assign different weights respectively according to the importance to the treatment effect, and obtain the weighted result of the overall adjustment frequency;

[0017] Set a threshold for the overall adjustment frequency. If the overall adjustment frequency of a patient reaches or exceeds the set threshold, the patient is considered to belong to the group with frequent treatment parameter adjustments.

[0018] In a preferred embodiment, analyze the temporal variation of the treatment plan of the group with frequent treatment parameter adjustments during the treatment cycle to evaluate the internal matching degree of the treatment plan for bone tissue repair, specifically:

[0019] Extract the treatment plan data of the group with frequent treatment parameter adjustments during the treatment cycle to establish a temporal data set;

[0020] According to the time dimension of the treatment parameters, construct a parameter temporal distribution curve during the treatment cycle;

[0021] Analyze the correlation between the change trend of the parameters in the temporal curve and the phased requirements of bone tissue repair;

[0022] Based on the correlation analysis, calculate the contribution matching degree index of treatment parameters to the repair effect: Based on the matching degree of each treatment parameter, calculate the comprehensive matching degree coefficient of all treatment parameters to the repair effect:

[0023] Among them, CMC is the comprehensive matching degree coefficient; M k is the matching degree of each treatment parameter; Q k is the weight of the k-th treatment parameter; n is the total number of treatment parameters.

[0024] In a preferred embodiment, analyze the real-time feedback data during the treatment process of the group with frequent treatment parameter adjustments, and evaluate the response divergence degree of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters. Specifically:

[0025] Collect the real-time feedback data of the group with frequent treatment parameter adjustments during the treatment process to construct a feedback data set;

[0026] Extract features from the feedback data, including physiological signal amplitude, frequency, and response delay;

[0027] Perform cluster analysis on the feedback features under the stimulation of the same type of treatment parameters to identify the response patterns of the patient group;

[0028] Calculate the dispersion degree of the feedback features, and evaluate the response divergence degree of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters: Based on the clustering results, calculate the dispersion degree of the feedback features under the stimulation of the same type of treatment parameters. The calculation formula is: Among them, V x represents the dispersion degree of the feedback features of the x-th type of treatment parameter stimulation; M represents the total number of feedback features belonging to the x-th type of treatment parameter stimulation in the clustering results; z y represents the actual value of the y-th feedback feature data; μ x represents the average value of the feedback features of the x-th type of treatment parameter stimulation.

[0029] In a preferred embodiment, based on the internal matching degree of the treatment plan to bone tissue repair and the response divergence degree of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters, judge the acceptance stability of the group with frequent treatment parameter adjustments to the treatment plan. Specifically:

[0030] Preset a comprehensive matching degree coefficient threshold, and compare the comprehensive matching degree coefficient with the comprehensive matching degree coefficient threshold:

[0031] When the comprehensive matching degree coefficient is greater than the comprehensive matching degree coefficient threshold, it indicates that the internal matching degree of the treatment plan to bone tissue repair is relatively high;

[0032] When the comprehensive matching degree coefficient is less than or equal to the comprehensive matching degree coefficient threshold, it indicates that the internal matching degree of the treatment plan for bone tissue repair is low;

[0033] Preset a dispersion threshold, and compare the dispersion with the dispersion threshold:

[0034] When the dispersion is greater than or equal to the dispersion threshold, it indicates that the response divergence degree of the group with frequent treatment parameter adjustment is relatively large under the stimulation of the same type of treatment parameters;

[0035] When the dispersion is less than the dispersion threshold, it indicates that the response divergence degree of the group with frequent treatment parameter adjustment is relatively low under the stimulation of the same type of treatment parameters;

[0036] When the comprehensive matching degree coefficient is greater than the comprehensive matching degree coefficient threshold and the dispersion is less than the dispersion threshold, the acceptance stability of the group with frequent treatment parameter adjustment for the treatment plan is high acceptance stability; otherwise, the acceptance stability of the group with frequent treatment parameter adjustment for the treatment plan is low acceptance stability.

[0037] In a preferred embodiment, when the acceptance stability of the group with frequent treatment parameter adjustment for the treatment plan is low acceptance stability, by analyzing the complexity of the sensing signals collected by the bone trauma treatment instrument during the treatment process, the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy is evaluated, specifically:

[0038] Collect multi-channel sensing signals of the bone trauma treatment instrument during the treatment process;

[0039] Preprocess the signal data and extract the feature signals;

[0040] Calculate the temporal entropy of each sensing signal to evaluate the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy: For the preprocessed signal sequence G abc , use the Shannon entropy formula to calculate the temporal entropy, and the calculation formula is: Among them, E abc represents the temporal entropy value of the a-th channel, the b-th time point, and the c-th signal type; p d represents the probability of the d-th value in the time series; m represents the total number of all values in the time series.

[0041] In a preferred embodiment, comprehensively analyze the internal matching degree of the treatment plan for bone tissue repair, the response divergence degree of the group with frequent treatment parameter adjustment under the stimulation of the same type of treatment parameters, and the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument, specifically:

[0042] Normalize the comprehensive matching degree coefficient corresponding to the internal matching degree of the treatment plan for bone tissue repair, the divergence corresponding to the response divergence of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters, and the temporal entropy value corresponding to the necessity of the bone trauma treatment instrument to enhance the diversified regulation strategy. Then, comprehensively analyze the normalized comprehensive matching degree coefficient, divergence, and temporal entropy value to calculate the regulation demand coefficient. The calculation formula for the regulation demand coefficient is: CDRI = α * (1 - CMC) + β * Vx + δ * Eabc|; where CDRI represents the regulation demand coefficient; CMC is the comprehensive matching degree coefficient; V x represents the divergence; E abc represents the temporal entropy value; α, β, and δ are the weight coefficients of the comprehensive matching degree coefficient, divergence, and temporal entropy value respectively;

[0043] Preset the regulation demand coefficient threshold and compare the regulation demand coefficient with the threshold:

[0044] When the regulation demand coefficient is greater than or equal to the regulation demand coefficient threshold, it is necessary to dynamically adjust the treatment parameter configuration strategy of the bone trauma treatment instrument, including optimizing the treatment parameter range, increasing personalized regulation strategies, and improving the fineness of signal acquisition and processing;

[0045] When the regulation demand coefficient is less than the regulation demand coefficient threshold, there is no need to dynamically adjust the treatment parameter configuration strategy of the bone trauma treatment instrument.

[0046] On the other hand, the present invention provides an intelligent control system for a bone trauma treatment instrument, including a data processing module, a temporal analysis module, a feedback analysis module, an acceptance stability judgment module, a complexity evaluation module, and a comprehensive regulation module;

[0047] Data processing module: Based on the patient data set stored inside the bone trauma treatment instrument, screen and classify the patients receiving bone trauma treatment to obtain the group with frequent treatment parameter adjustments;

[0048] Temporal analysis module: Analyze the temporal changes of the treatment plan of the group with frequent treatment parameter adjustments during the treatment cycle to evaluate the internal matching degree of the treatment plan for bone tissue repair;

[0049] Feedback analysis module: Analyze the real-time feedback data during the treatment process of the group with frequent treatment parameter adjustments to evaluate the response divergence of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters.

[0050] Acceptance stability judgment module: Based on the internal matching degree of the treatment plan for bone tissue repair and the response divergence of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters, judge the acceptance stability of the group with frequent treatment parameter adjustments for the treatment plan;

[0051] Complexity evaluation module: When the acceptance stability of the treatment plan for the group with frequent adjustment of treatment parameters is low acceptance stability, evaluate the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy by analyzing the complexity of the sensing signals collected during the treatment process by the bone trauma treatment instrument;

[0052] Comprehensive regulation module: Comprehensively analyze the internal matching degree of the treatment plan for bone tissue repair, the response divergence of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, and the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument.

[0053] Technical effects and advantages of an intelligent control method and system for a bone trauma treatment instrument of the present invention:

[0054] Through the screening and classification based on patient data, accurately identify the group with frequent adjustment of treatment parameters, make the treatment plan more targeted, and improve the scientific nature of patient grouping; combine the analysis of the sequential changes of the treatment plan and the evaluation of the divergence of real-time feedback data to comprehensively judge the acceptance stability of the treatment plan for the patient group, and solve the problem of insufficient personalization in the traditional treatment mode; by analyzing the complexity of multi-channel sensing signals, scientifically evaluate the necessity of the treatment instrument to improve the diversified regulation strategy, and provide a theoretical basis for the dynamic optimization in the complex treatment process; through the comprehensive analysis of the matching degree of bone tissue repair, patient response divergence and signal complexity, realize the dynamic adjustment of treatment parameters, not only meet the personalized needs of patients, but also improve the accuracy and effect of treatment, can effectively shorten the bone tissue repair cycle, improve the treatment safety and efficiency, and promote the development of the field of bone trauma intelligent medical equipment. Brief description of the drawings

[0055] Figure 1 It is a schematic diagram of an intelligent control method for a bone trauma treatment instrument of the present invention;

[0056] Figure 2 It is a schematic diagram of the structure of an intelligent control system for a bone trauma treatment instrument of the present invention. Detailed implementation manners

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiment 1

[0059] Figure 1A smart control method for a bone trauma treatment device according to the present invention is provided, which includes the following steps:

[0060] Based on the patient dataset stored in the bone trauma treatment device, screen and classify the patients receiving bone trauma treatment to obtain a group with frequent adjustment of treatment parameters;

[0061] Analyze the temporal changes of the treatment plan of the group with frequent adjustment of treatment parameters during the treatment cycle to evaluate the internal matching degree of the treatment plan for bone tissue repair;

[0062] Analyze the real-time feedback data during the treatment process of the group with frequent adjustment of treatment parameters to evaluate the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters;

[0063] Based on the internal matching degree of the treatment plan for bone tissue repair and the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, judge the acceptance stability of the group with frequent adjustment of treatment parameters for the treatment plan;

[0064] When the acceptance stability of the group with frequent adjustment of treatment parameters for the treatment plan is low acceptance stability, evaluate the necessity of the bone trauma treatment device to improve the diversified regulation strategy by analyzing the complexity of the sensing signals collected during the treatment process;

[0065] Comprehensively analyze the internal matching degree of the treatment plan for bone tissue repair, the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, and the necessity of the bone trauma treatment device to improve the diversified regulation strategy, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment device.

[0066] Specifically, based on the patient dataset stored in the bone trauma treatment device, screen and classify the patients receiving bone trauma treatment to obtain a group with frequent adjustment of treatment parameters, including:

[0067] Extract the treatment data of all patients from the database of the bone trauma treatment device: The database of the bone trauma treatment device contains the historical information of patient treatment, including treatment time, treatment method, initial values and adjustment records of treatment parameters, patient physiological feedback signals, etc. The extracted data is stored in a structured form, covering the basic information of patients (such as age, gender, medical history) and personalized treatment parameters (such as mechanical vibration frequency, ultrasonic stimulation intensity, electric field stimulation level, biological factor release cycle, etc.). When extracting data, a unified coding rule is used to map the fields, and each piece of data is assigned a unique identifier.

[0068] Clean the extracted data to exclude duplicate, invalid or abnormal data: Screen and eliminate the records with missing values in the data, or fill in the missing values using a reasonable interpolation method;

[0069] Check for duplicate records in the data and merge the duplicate data based on the patient's unique identifier;

[0070] Perform outlier detection on the time series data of treatment parameters, and use statistical methods (such as the three - standard - deviation method) to identify and remove values that exceed the reasonable range;

[0071] Verify whether the patient's treatment information is complete to ensure that all key treatment parameters are recorded and comply with the specifications of the treatment instrument.

[0072] Based on the patient's treatment records and the frequency of parameter changes, design a screening rule to screen the group of patients with frequent adjustment of treatment parameters: According to the patient's treatment records, extract the adjustment information of all treatment parameters, including the number of adjustments of each parameter and the total duration of the treatment cycle. By analyzing the adjustment information of all treatment parameters, design a rule for measuring the frequency of treatment parameter adjustment.

[0073] For each patient and for each treatment parameter, calculate the adjustment frequency of the parameter during the entire treatment cycle. The adjustment frequency is defined as the number of adjustments of the parameter divided by the total duration of the treatment cycle. According to the adjustment frequencies of all treatment parameters, different weights are assigned to each parameter according to its importance to the treatment effect, and the weighted result of the overall adjustment frequency is obtained.

[0074] Set the threshold standard for the overall adjustment frequency to screen the group of patients with frequent adjustment of treatment parameters. If the overall adjustment frequency of a certain patient reaches or exceeds the set threshold, then this patient is considered to belong to the group of patients with frequent adjustment of treatment parameters. The design of the screening rule needs to combine the opinions of clinical experts and the actual treatment effects of patients to ensure the scientificity and applicability of the screening criteria.

[0075] Specifically, analyze the temporal changes of the treatment plan within the treatment cycle for the group of patients with frequent adjustment of treatment parameters, and evaluate the internal matching degree of the treatment plan for bone tissue repair, including:

[0076] Extract the treatment plan data of the group of patients with frequent adjustment of treatment parameters within the treatment cycle and establish a time - series data set: From the records of the group of patients with frequent adjustment of treatment parameters, extract all the treatment plan data within the treatment cycle. Each piece of treatment plan data contains multiple treatment parameters, and these parameters include mechanical micro - vibration frequency, ultrasonic intensity, electric field stimulation level, and biological factor release cycle, etc. Uniformly encode these data to ensure the consistency and integrity of the data structure.

[0077] Sort the treatment plan data in the time dimension to establish a time - series data set, denoted as D(t), where t is the treatment time. Each time t contains multiple treatment parameter values, denoted as {P t1 ,P t2 ,...P tn}, where P tk represents the k-th treatment parameter at time t, and n is the total number of treatment parameters.

[0078] According to the time dimension of the treatment parameters, construct the parameter time series distribution curve within the treatment cycle: Based on the time series dataset D(t), construct the time series distribution curve of each treatment parameter within the treatment cycle. For each treatment parameter Ptk, plot the time series data {Pt 1 , Pt 2 ,... Ptn} against time t to form a curve C k (t). The curve C k (t) represents the dynamic change of the k-th treatment parameter within the treatment cycle.

[0079] To avoid the influence of data noise, smoothing processing is required before curve construction. The moving average method is used to calculate the treatment parameter value at each time point: where W represents the window width, indicating the size of the time window for smoothing processing; C k (t) represents the smoothed value of the k-th treatment parameter at time t; P ki represents the actual value of the k-th treatment parameter at time i.

[0080] Analyze the correlation between the change trend of the parameters in the time series curve and the phased requirements of bone tissue repair: Based on the time series distribution curve C k (t), combined with the phased requirement model of bone tissue repair, evaluate the matching situation between the treatment parameters and the repair requirements. The repair requirement model is based on the typical stages of bone repair (such as the inflammation stage, repair stage, and reconstruction stage), and defines the ideal requirement range R k (t) for each stage.

[0081] Through the matching degree function, calculate the correlation between the time series distribution curve C k (t) and the ideal requirement range R k (t) of the treatment parameters: where Mk represents the matching degree of the k-th treatment parameter; t1 and t2 represent the start time and end time of the treatment cycle respectively; min(C k (t), R k (t)) represents the minimum value between the smoothed value of the k-th treatment parameter and the corresponding ideal requirement range at time t.

[0082] Based on the correlation analysis, calculate the contribution matching degree index of the treatment parameters to the repair effect: Based on the matching degree M k of each treatment parameter, calculate the comprehensive matching degree coefficient of all treatment parameters to the repair effect:

[0083] where CMC is the comprehensive matching degree coefficient; Mk The matching degree for each treatment parameter; Q k is the weight of the k-th treatment parameter, representing the relative importance of the corresponding treatment parameter to the bone tissue repair effect; n is the total number of treatment parameters.

[0084] The larger the comprehensive matching degree coefficient, the higher the internal matching degree of the treatment plan for bone tissue repair. The larger the comprehensive matching degree coefficient, the more consistent the temporal variation trend of the treatment parameters with the requirements of different stages of bone tissue repair. For example, in the inflammatory, repair, and reconstruction stages of bone tissue, each stage may have different ideal ranges and dynamic change requirements for vibration frequency, ultrasonic intensity, or electric field stimulation level. When the actual values of the treatment parameters can be adjusted more accurately following these stage requirements and the overlap degree of the temporal distribution with the demand curve is relatively high, the value of the comprehensive matching degree coefficient also increases accordingly. This indicates that the treatment plan can not only accurately respond to the physiological requirements of bone tissue repair but also provide more personalized and dynamic support, thereby improving the treatment effect, accelerating the repair process, and reducing unnecessary interventions or possible side effects.

[0085] Specifically, analyze the real-time feedback data during the treatment process of the group with frequent treatment parameter adjustments, and evaluate the response divergence degree of the group with frequent treatment parameter adjustments under the stimulation of similar treatment parameters, including:

[0086] Collect the real-time feedback data during the treatment process of the group with frequent treatment parameter adjustments to construct a feedback data set: During the treatment process of the group with frequent treatment parameter adjustments, collect real-time feedback data, including the physiological signals of patients (such as electrophysiological responses, blood flow change signals) and the physical feedback signals collected by the treatment instrument (such as force sensor readings). These data are classified and labeled according to the patient number, treatment time, and stimulation parameter category.

[0087] Construct a feedback data set F uvw , where u represents the u-th patient; v represents the v-th treatment time; w represents the type of feedback signal, such as physiological signal or physical signal.

[0088] Extract features from the feedback data, including physiological signal amplitude, frequency, and response delay: For the feedback data set F uvw , use the fast Fourier transform to extract the key features of the real-time feedback signal, including:

[0089] Signal amplitude A uvw , the maximum intensity of the physiological response or physical feedback caused by the treatment stimulation;

[0090] Signal frequency f uvw , the periodic change of the signal in the time dimension;

[0091] Response delay T uvw, the time delay when the feedback signal reaches the peak after the therapeutic stimulation.

[0092] The feature dataset after feature extraction is denoted as D uvw ={A uvw , f uvw , T uvw}}.

[0093] Perform clustering analysis on the feedback features under the stimulation of the same type of treatment parameters to identify the response patterns of the patient group: According to the feedback feature dataset D uvw , group the data according to the type of treatment parameters. For example, mechanical vibration stimulation group, ultrasonic stimulation group, electric field stimulation group, etc. For each group of data, apply an unsupervised clustering algorithm (such as K-means algorithm) for clustering analysis to identify the response patterns of the patient group under the same treatment parameters.

[0094] The clustering steps include:

[0095] 1. Initialize the clustering center;

[0096] 2. Calculate the Euclidean distance from each feature data to the clustering center and assign it to the nearest cluster;

[0097] 3. Update the clustering center to minimize the distance within the group;

[0098] 4. Repeat steps 2 and 3 until the clustering result converges.

[0099] The clustering result divides the feedback features of the patients into different patterns, and each pattern represents a typical response behavior.

[0100] Calculate the dispersion of the feedback features to evaluate the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same type of treatment parameters: Based on the clustering result, calculate the dispersion of the feedback features under the stimulation of the same type of treatment parameters, and the calculation formula is: where V x represents the dispersion of the feedback features of the x-th type of treatment parameter stimulation; M represents the total number of feedback features belonging to the x-th type of treatment parameter stimulation in the clustering result; z y represents the actual value of the y-th feedback feature data; μ x represents the average value of the feedback features of the x-th type of treatment parameter stimulation.

[0101] The greater the dispersion, the greater the divergence of the responses of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters, that is, the group with frequent treatment parameter adjustments shows more obvious differences in response to the stimulation of the same treatment parameters. A larger dispersion reflects an increase in the discreteness of the statistical distribution of the physiological or physical feedback characteristics of the patient group, indicating that some patients have a lower adaptability to the current treatment parameters. A larger dispersion usually means that a more refined parameter grouping or dynamic adjustment mechanism is needed to optimize the applicability and effectiveness of the treatment plan.

[0102] Specifically, based on the inherent matching degree of the treatment plan for bone tissue repair and the divergence of the responses of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters, judge the acceptance stability of the group with frequent treatment parameter adjustments for the treatment plan, including:

[0103] Preset a comprehensive matching degree coefficient threshold, and compare the comprehensive matching degree coefficient with the comprehensive matching degree coefficient threshold:

[0104] When the comprehensive matching degree coefficient is greater than the comprehensive matching degree coefficient threshold, it indicates that the inherent matching degree of the treatment plan for bone tissue repair is relatively high, the temporal changes of the treatment parameters highly coincide with the requirements of each stage of bone tissue repair, and it can effectively support the healing and functional recovery of bone tissue;

[0105] When the comprehensive matching degree coefficient is less than or equal to the comprehensive matching degree coefficient threshold, it indicates that the inherent matching degree of the treatment plan for bone tissue repair is relatively low, and the setting of the treatment parameters does not fully meet the requirements during the bone tissue repair process;

[0106] The setting of the comprehensive matching degree coefficient threshold is based on the clinical requirements for bone tissue repair, the statistical analysis of historical treatment data, and expert experience. Usually, by analyzing the distribution of the comprehensive matching degree coefficients of different treatment plans and combining the minimum effective requirements for bone repair, a value that can reflect the applicability of the treatment plan is selected as the threshold.

[0107] Preset a dispersion threshold, and compare the dispersion with the dispersion threshold:

[0108] When the dispersion is greater than or equal to the dispersion threshold, it indicates that the divergence of the responses of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters is relatively large, and there are obvious differences in the feedback of patients to the same stimulation parameters;

[0109] When the dispersion is less than the dispersion threshold, it indicates that the divergence of the responses of the group with frequent treatment parameter adjustments under the stimulation of the same type of treatment parameters is relatively low, and most patients have relatively similar responses to the current treatment parameters;

[0110] The setting of the dispersion threshold is determined based on statistical analysis and clinical requirements. By calculating the dispersion of historical feedback data from a large number of patient groups and combining the evaluation of treatment effects, a value that can reflect the balance point between response consistency and difference is selected as the threshold.

[0111] When the comprehensive matching degree coefficient is greater than the comprehensive matching degree coefficient threshold and the dispersion is less than the dispersion threshold, the acceptance stability of the treatment parameter adjustment frequent group for the treatment plan is high acceptance stability; otherwise, the acceptance stability of the treatment parameter adjustment frequent group for the treatment plan is low acceptance stability.

[0112] Specifically, when the acceptance stability of the treatment parameter adjustment frequent group for the treatment plan is low acceptance stability, by analyzing the complexity of the sensing signals collected by the bone trauma treatment instrument during the treatment process, the necessity of improving the diversified regulation strategy of the bone trauma treatment instrument is evaluated, including:

[0113] Collect multi-channel sensing signals of the bone trauma treatment instrument during the treatment process: During the bone trauma treatment process, the multi-channel sensing signals collected by the treatment instrument include the real-time outputs of mechanical vibration sensors, mechanical sensors, bioelectric sensors, and temperature sensors. Each type of signal represents different interaction characteristics between the treatment instrument and the patient. For example, mechanical vibration feedback reflects the effect of external force, and bioelectric signals characterize nerve or muscle responses. Record these signals and label information such as signal sources and parameter settings to form structured signal data, denoted as S abc , where a represents the channel number of the signal (such as mechanical, mechanical, bioelectric, etc.); b represents the acquisition time point; c represents the signal type.

[0114] Preprocess the signal data and extract feature signals: Use a band-pass filter to filter signal noise and retain the effective frequency band within the operating range of the treatment instrument;

[0115] Normalize the signals to make the signal amplitude ranges of all channels consistent;

[0116] Apply wavelet transform to decompose the signals and extract features in specific frequency bands;

[0117] Divide the processed signals into time segments to generate a feature sequence G abc , and the signal features of each segment include amplitude, frequency, and spectral energy distribution.

[0118] Calculate the temporal entropy of each sensing signal to evaluate the necessity of improving the diversified regulation strategy of the bone trauma treatment instrument: For the preprocessed signal sequence G abc , use the Shannon entropy formula to calculate the temporal entropy, and the calculation formula is: where E abc represents the temporal entropy value of the a-th channel, b-th time point, and c-th signal type; pd It represents the probability of the d-th value in the time series; m represents the total number of all values in the time series.

[0119] Compare the temporal entropy value of each sensing signal with a preset reference range:

[0120] When the temporal entropy value of each sensing signal is within the preset reference range, it indicates that the regulation strategy of the bone trauma treatment instrument can meet the requirements of signal diversity, and there is no need to improve the diversified regulation strategy;

[0121] When the temporal entropy value of each sensing signal is lower than the preset reference range, it indicates that the complexity of the signal is insufficient, and there may be overly simple or highly repetitive features, resulting in insufficient information in the signal and unable to fully reflect the dynamic requirements of the treatment process. The regulation strategy of the bone trauma treatment instrument cannot meet the requirements of signal diversity, and it is necessary to improve the diversified regulation strategy;

[0122] When the temporal entropy value of each sensing signal is higher than the preset reference range, it indicates that the complexity of the signal is too high, and it may contain too much noise or random components, resulting in an increase in the difficulty of extracting effective information. The regulation strategy of the bone trauma treatment instrument cannot meet the requirements of signal diversity, and it is necessary to improve the diversified regulation strategy.

[0123] Specifically, comprehensively analyze the internal matching degree of the treatment plan for bone tissue repair, the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, and the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument, including:

[0124] Normalize the comprehensive matching degree coefficient corresponding to the internal matching degree of the treatment plan for bone tissue repair, the dispersion degree corresponding to the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, and the temporal entropy value corresponding to the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy. Conduct a comprehensive analysis of the normalized comprehensive matching degree coefficient, dispersion degree, and temporal entropy value, and calculate the regulation demand coefficient. According to the regulation demand coefficient, dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument.

[0125] The calculation formula of the regulation demand coefficient is: CDRI = α * (1 - CMC) + β * Vx + δ * Eabc|; where, CDRI represents the regulation demand coefficient; CMC is the comprehensive matching degree coefficient; V x represents the dispersion degree; E abc represents the temporal entropy value; α, β, δ are the weight coefficients of the comprehensive matching degree coefficient, dispersion degree, and temporal entropy value respectively.

[0126] Preset a regulation demand coefficient threshold, and compare the regulation demand coefficient with the regulation demand coefficient threshold:

[0127] When the regulation demand coefficient is greater than or equal to the regulation demand coefficient threshold, it is necessary to dynamically adjust the treatment parameter configuration strategy of the bone trauma treatment instrument, including optimizing the treatment parameter range, adding personalized regulation strategies, and improving the fineness of signal acquisition and processing to meet the actual needs of patients and improve the treatment effect;

[0128] When the regulation demand coefficient is less than the regulation demand coefficient threshold, it is not necessary to dynamically adjust the treatment parameter configuration strategy of the bone trauma treatment instrument, and the existing configuration strategy is maintained to ensure the stability of treatment.

[0129] Embodiment 2

[0130] The difference between Embodiment 2 and Embodiment 1 of the present invention is that this embodiment introduces an intelligent control system for a bone trauma treatment instrument.

[0131] Figure 2 The structural schematic diagram of an intelligent control system for a bone trauma treatment instrument of the present invention is given. An intelligent control system for a bone trauma treatment instrument includes a data processing module, a timing analysis module, a feedback analysis module, an acceptance stability judgment module, a complexity evaluation module, and a comprehensive regulation module;

[0132] Data processing module: Based on the patient data set stored inside the bone trauma treatment instrument, screen and classify the patients receiving bone trauma treatment to obtain the group with frequent adjustment of treatment parameters;

[0133] Timing analysis module: Analyze the timing changes of the treatment plan of the group with frequent adjustment of treatment parameters during the treatment cycle, and evaluate the internal matching degree of the treatment plan for bone tissue repair;

[0134] Feedback analysis module: Analyze the real-time feedback data during the treatment process of the group with frequent adjustment of treatment parameters, and evaluate the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters.

[0135] Acceptance stability judgment module: Based on the internal matching degree of the treatment plan for bone tissue repair and the response divergence degree of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, judge the acceptance stability of the group with frequent adjustment of treatment parameters for the treatment plan;

[0136] Complexity evaluation module: When the acceptance stability of the group with frequent adjustment of treatment parameters for the treatment plan is low acceptance stability, evaluate the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy by analyzing the complexity of the sensing signals collected during the treatment process;

[0137] Comprehensive regulation module: comprehensively analyze the inherent matching degree of the treatment plan for bone tissue repair, the response divergence of the group with frequent adjustment of treatment parameters under the stimulation of the same type of treatment parameters, and the necessity of the bone trauma treatment instrument to enhance diversified regulation strategies, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument.

[0138] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0139] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0140] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0141] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0142] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in electrical, mechanical, or other forms.

[0143] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0145] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present application, or the part that contributes to the prior art, or this part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0146] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0147] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent control method for a bone trauma treatment instrument, characterized in that: The steps include: Based on the patient data set stored in the bone trauma treatment instrument, the patients receiving bone trauma treatment are screened and classified to obtain the group with frequent treatment parameter adjustment; The temporal changes of the treatment regimens of the group with frequent treatment parameter adjustments during the treatment cycle were analyzed to evaluate the intrinsic matching degree of the treatment regimens to bone tissue repair, specifically: Extract the treatment plan data of the group with frequent treatment parameter adjustment during the treatment cycle and establish a time series data set; According to the time dimension of treatment parameters, the time series distribution curve of parameters within the treatment cycle is constructed; Analyze the correlation between the changing trend of parameters in the time series curve and the staged demand for bone tissue repair; Based on correlation analysis, calculate the matching degree index of the contribution of treatment parameters to the repair effect: Based on the matching degree of each treatment parameter, calculate the comprehensive matching coefficient of all treatment parameters to the repair effect: ;in, is the comprehensive matching coefficient; for the degree of fit of each treatment parameter; For the The weights of the treatment parameters; is the total number of treatment parameters; The real-time feedback data of the treatment process of the group with frequent treatment parameter adjustment was analyzed to evaluate the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameters, specifically: Collect real-time feedback data from the group with frequent treatment parameter adjustments during the treatment process and build a feedback data set; Extract features from feedback data, including physiological signal amplitude, frequency, and response delay; Cluster analysis of feedback characteristics under stimulation of similar treatment parameters to identify response patterns of patient groups; Calculate the discreteness of feedback characteristics and evaluate the response divergence of the frequent treatment parameter adjustment group under the stimulation of similar treatment parameters: Based on the clustering results, calculate the discreteness of feedback characteristics under the stimulation of similar treatment parameters. The calculation formula is: ;in, Indicates Discreteness of feedback characteristics of quasi-therapy parameter stimulation; Indicates that the clustering result belongs to the total number of feedback features of the class treatment parameter stimulation; Indicates The actual value of the feedback feature data; Indicates The average of the feedback characteristics of the class treatment parameter stimulation; Based on the intrinsic matching degree of the treatment plan to bone tissue repair and the response divergence of the group with frequent treatment parameter adjustments under the stimulation of similar treatment parameters, the stability of the group with frequent treatment parameter adjustments in accepting the treatment plan is determined; When the acceptance stability of the treatment plan for the group with frequent treatment parameter adjustment is low, the necessity of improving the diversified regulation strategy of the bone trauma treatment instrument is evaluated by analyzing the complexity of the sensor signals collected by the bone trauma treatment instrument during the treatment process; A comprehensive analysis is conducted on the intrinsic matching degree of the treatment plan for bone tissue repair, the response divergence of the group with frequent treatment parameter adjustments under the stimulation of similar treatment parameters, and the necessity of improving the diversified regulation strategy of the bone trauma treatment instrument, so as to dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment instrument.

2. The intelligent control method of a bone trauma treatment instrument according to claim 1, characterized in that: Based on the patient data set stored in the bone trauma treatment instrument, the patients receiving bone trauma treatment were screened and classified to obtain the group with frequent treatment parameter adjustment, specifically: Extract the treatment data of all patients from the database of the bone trauma treatment instrument; Clean the extracted data to exclude duplicate, invalid or abnormal data; Based on the patient treatment records and parameter change frequency, screening rules are designed to screen the patient group whose treatment parameters are frequently adjusted during the treatment process: for each patient, for each treatment parameter, the adjustment frequency of the parameter in the entire treatment cycle is calculated, and the adjustment frequency is defined as the number of adjustments of the parameter divided by the total duration of the treatment cycle; according to the adjustment frequency of all treatment parameters, different weights are assigned according to their importance to the treatment effect, and the weighted result of the overall adjustment frequency is obtained; A threshold for the overall adjustment frequency is set. If the patient's overall adjustment frequency reaches or exceeds the set threshold, the patient is considered to belong to the group with frequent treatment parameter adjustments.

3. The intelligent control method of a bone trauma treatment instrument according to claim 2, characterized in that: Based on the intrinsic matching degree of the treatment plan to bone tissue repair and the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameters, the stability of the group with frequent treatment parameter adjustment to the treatment plan is judged, specifically: Preset the comprehensive matching coefficient threshold, and compare the comprehensive matching coefficient with the comprehensive matching coefficient threshold: When the comprehensive matching coefficient is greater than the comprehensive matching coefficient threshold, it indicates that the intrinsic matching degree of the treatment plan to bone tissue repair is high; When the comprehensive matching coefficient is less than or equal to the comprehensive matching coefficient threshold, it indicates that the intrinsic matching degree of the treatment plan to bone tissue repair is low; Preset the discreteness threshold and compare the discreteness with the discreteness threshold: When the dispersion is greater than or equal to the dispersion threshold, it means that the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameters is large; When the dispersion is less than the dispersion threshold, it means that the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameters is low; When the comprehensive matching coefficient is greater than the comprehensive matching coefficient threshold and the dispersion is less than the dispersion threshold, the acceptance stability of the treatment plan by the group with frequent treatment parameter adjustment is high acceptance stability; otherwise, the acceptance stability of the treatment plan by the group with frequent treatment parameter adjustment is low acceptance stability.

4. The intelligent control method of a bone trauma treatment instrument according to claim 3, characterized in that: When the acceptance stability of the treatment plan for the group with frequent treatment parameter adjustment is low, the necessity of improving the diversified regulation strategy of the bone trauma therapeutic instrument is evaluated by analyzing the complexity of the sensor signals collected by the bone trauma therapeutic instrument during the treatment process, specifically: Collect multi-channel sensor signals of the bone trauma treatment device during the treatment process; Preprocess the signal data and extract characteristic signals; Calculate the temporal entropy of each sensor signal to evaluate the necessity of improving the diversified regulation strategy of the bone trauma treatment device: , the Shannon entropy formula is used to calculate the time series entropy, the calculation formula is: ;in, Indicates Channel, Time point, Temporal entropy value of signal type; Indicates the time series The probability of taking a value; Represents the total number of all values ​​in the time series.

5. The intelligent control method of a bone trauma treatment instrument according to claim 4, characterized in that: A comprehensive analysis is conducted on the intrinsic matching degree of the treatment plan to bone tissue repair, the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameter, and the necessity of improving the diversified regulation strategy of the bone trauma treatment instrument, and the configuration strategy of dynamically adjusting the treatment parameters of the bone trauma treatment instrument is as follows: The comprehensive matching coefficient corresponding to the intrinsic matching degree of the treatment plan for bone tissue repair, the discreteness corresponding to the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameter, and the time series entropy value corresponding to the necessity of the bone trauma treatment instrument to improve the diversified regulation strategy are normalized, and the normalized comprehensive matching coefficient, discreteness and time series entropy value are comprehensively analyzed to calculate the regulation demand coefficient. The calculation formula of the regulation demand coefficient is: ;in, represents the control demand coefficient; is the comprehensive matching coefficient; Indicates discreteness; Represents the time series entropy value; They are the weight coefficients of comprehensive matching coefficient, dispersion and time series entropy value respectively; Preset the control demand coefficient threshold and compare the control demand coefficient with the control demand coefficient threshold: When the control demand coefficient is greater than or equal to the control demand coefficient threshold, it is necessary to dynamically adjust the treatment parameter configuration strategy of the bone trauma treatment instrument, including optimizing the treatment parameter range, adding personalized control strategies, and improving the precision of signal acquisition and processing; When the regulation demand coefficient is less than the regulation demand coefficient threshold, there is no need to dynamically adjust the treatment parameter configuration strategy of the bone trauma treatment device.

6. An intelligent control system for a bone trauma treatment instrument, used to implement the intelligent control method for a bone trauma treatment instrument according to any one of claims 1 to 5, characterized in that: It includes data processing module, timing analysis module, feedback analysis module, acceptance stability judgment module, complexity assessment module and comprehensive control module; Data processing module: based on the patient data set stored in the bone trauma treatment instrument, screen and classify the patients receiving bone trauma treatment to obtain the group with frequent treatment parameter adjustment; Timing analysis module: Analyze the timing changes of treatment plans for groups with frequent treatment parameter adjustments during the treatment cycle to evaluate the intrinsic matching degree of the treatment plans for bone tissue repair; Feedback analysis module: Analyze the real-time feedback data during the treatment of the group with frequent treatment parameter adjustments, and evaluate the response divergence of the group with frequent treatment parameter adjustments under the stimulation of the same treatment parameters; Acceptance stability judgment module: Based on the intrinsic matching degree of the treatment plan to bone tissue repair and the response divergence of the group with frequent treatment parameter adjustment under the stimulation of the same treatment parameters, the acceptance stability of the group with frequent treatment parameter adjustment to the treatment plan is judged; Complexity evaluation module: When the acceptance stability of the treatment plan for the group with frequent treatment parameter adjustment is low, the complexity of the sensor signals collected by the bone trauma treatment device during the treatment process is analyzed to evaluate the necessity of improving the diversified regulation strategy of the bone trauma treatment device; Comprehensive control module: Comprehensively analyze the intrinsic matching degree of the treatment plan to bone tissue repair, the response divergence of the group with frequent treatment parameter adjustment under the stimulation of similar treatment parameters, and the necessity of improving the diversified control strategy of the bone trauma treatment device, and dynamically adjust the configuration strategy of the treatment parameters of the bone trauma treatment device.

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