Acupoint and channel dredging cupping integrated device operation parameter intelligent control method and system

Abstract

The application provides an acupoint channel-stimulating cupping integrated device operation parameter intelligent control method and system, belongs to the technical field of intelligent control of medical devices, and comprises the following steps: acquiring user basic information of an operation object, such as age, physical type, skin thickness and operation site; determining site characteristic parameters according to the anatomical characteristics of the operation site; retrieving a recommended parameter interval from a parameter knowledge base; outputting a combination of operation parameters such as acupuncture depth, acupuncture speed, negative pressure intensity and negative pressure duration by adopting a method combining fuzzy reasoning and machine learning; dynamically adjusting the power of a negative pressure pump according to the deviation of an actual negative pressure curve from a preset target; setting upper and lower limit thresholds to realize safety protection; and saving operation parameters for subsequent optimization. The application realizes scientific, intelligent and individualized setting and closed-loop control of the operation parameters of the channel-stimulating cupping integrated device.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for medical devices, specifically to an intelligent control method and system for the operating parameters of an integrated acupuncture and cupping device. Background Technology

[0002] Bloodletting and cupping therapy is an important component of traditional Chinese medicine's external treatment methods. By pricking and bleeding specific acupoints combined with negative pressure suction, it can effectively promote local blood circulation, unblock meridians, and regulate organ function. With the development of medical device technology, integrated bloodletting and cupping devices combine acupuncture and negative pressure cupping modules into a single device, achieving continuous and automated execution of bloodletting and cupping operations. However, existing integrated bloodletting and cupping devices still have significant shortcomings in terms of operating parameter settings.

[0003] In existing technologies, such as the adjustable control cabinet simulation system for automated production lines disclosed in Chinese patent application CN120428676A, the system uses an intelligent parameter optimization device based on machine learning algorithms to achieve self-tuning of control parameters, and realizes closed-loop control of the internal thermodynamic parameters of the cabinet through an environmental adaptive subsystem. Although this technical solution provides a concept for adaptive parameter adjustment, it is aimed at the environmental parameter control of industrial control cabinets and cannot be directly applied to the problem of adapting to human physiological parameters in the field of medical devices.

[0004] Specifically, existing integrated bloodletting and cupping devices have the following technical problems: First, the setting of acupuncture depth and negative pressure intensity parameters is highly dependent on the operator's clinical experience and lacks scientific quantitative basis. Different operators may give significantly different parameter settings for the same patient. Second, existing devices fail to fully consider the individual differences of the patients, including the influence of factors such as age, body type, and skin thickness on the optimal operating parameters, resulting in a contradiction between the universality and individual suitability of parameter settings. Third, there are significant differences in the anatomical characteristics of different operation sites. For example, the subcutaneous tissue depth and vascular distribution characteristics of the back, abdomen, and limbs are different, and existing devices lack parameter adjustment mechanisms for site characteristics. Fourth, the parameter matching of existing devices mostly uses fixed reference tables or simple linear interpolation methods, which are difficult to handle nonlinear optimization problems under the interaction of multiple factors. Fifth, the device lacks an effective real-time feedback mechanism during operation and cannot dynamically adjust the control parameters according to the actual negative pressure establishment, resulting in a deviation between the negative pressure establishment curve and the preset target. Sixth, the safety protection mechanism of existing devices is relatively simple, mostly using single-parameter threshold judgment, and failing to establish a comprehensive safety protection system with multi-parameter linkage.

[0005] The aforementioned problems severely restrict the clinical application efficacy and safety of the integrated bloodletting and cupping device. Improper acupuncture depth settings may result in needles being inserted too superficially, failing to achieve the therapeutic effect, or too deeply, damaging deep tissues; improper negative pressure settings may lead to insufficient suction or skin damage. Therefore, there is an urgent need to develop an intelligent control method and system for the operating parameters of the integrated bloodletting and cupping device that comprehensively considers the individual characteristics of the patient and the anatomical features of the treatment site, achieves intelligent matching and closed-loop control of operating parameters, and possesses a comprehensive safety protection mechanism. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent control method and system for the operating parameters of an integrated acupuncture and cupping device. Through multi-factor fusion and intelligent optimization control framework, it solves the problems of the need for operator experience in setting the acupuncture depth and negative pressure intensity parameters of the integrated acupuncture and cupping device, and the impact of improper parameter matching on the device's working efficiency.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] The intelligent control method for the operating parameters of the integrated acupuncture and cupping device includes the following steps: First, a user information input step is performed to obtain the user's basic information, including age, body type, skin thickness, and the treatment site. Second, a site feature analysis step is performed to determine site feature parameters based on the anatomical characteristics of the treatment site, including the skin thickness range and subcutaneous tissue depth reference values. Third, a parameter knowledge base retrieval step is performed to retrieve recommended parameter ranges matching the user's basic information and site feature parameters from the parameter knowledge base. The parameter knowledge base stores recommended acupuncture depth and negative pressure intensity ranges for different sites and body types. The system performs several steps: First, it executes an intelligent parameter matching step, employing a combination of fuzzy inference and machine learning models to integrate user basic information and site feature parameters to output a combination of operating parameters, including acupuncture depth, acupuncture speed, negative pressure intensity, and negative pressure duration. Second, it executes a real-time feedback control step, collecting data from negative pressure and displacement sensors during device operation and dynamically adjusting the negative pressure pump power based on the deviation between the actual negative pressure curve and the preset target. Third, it executes a safety protection step, setting upper and lower thresholds for each parameter; when any parameter exceeds the upper or lower threshold, the system automatically stops and releases the negative pressure. Finally, it executes a parameter recording step, saving the operating parameters for subsequent analysis and optimization.

[0009] An intelligent control system for the operating parameters of an integrated acupuncture and cupping device includes: a user information input module for acquiring basic user information of the user; a site feature analysis module for determining site feature parameters based on the anatomical features of the operating site; a parameter knowledge base module for storing and retrieving recommended parameter ranges under different conditions; a parameter intelligent matching module for outputting combinations of operating parameters using a combination of fuzzy reasoning and machine learning models; a real-time feedback control module for dynamically adjusting the negative pressure pump power based on the deviation between the actual value and the preset target; a safety protection module for monitoring each parameter and executing protective actions when limits are exceeded; and a parameter recording module for saving operating parameters for subsequent analysis.

[0010] The beneficial effects of this invention are as follows: First, by using a multi-factor fusion-based intelligent parameter matching method, the invention comprehensively considers individualized factors such as the age, body type, skin thickness, and operation site of the patient, thereby improving the scientific nature and individual adaptability of the operating parameters. Second, by introducing an anatomical-based site feature analysis mechanism, the invention provides differentiated parameter recommendations based on the differences in subcutaneous tissue structure at different operation sites, effectively avoiding improper parameter settings due to neglect of site features. Third, by employing an intelligent matching method combining fuzzy reasoning and machine learning, the invention integrates expert clinical experience and achieves data-driven parameter optimization, improving the accuracy and robustness of parameter matching. Fourth, by establishing a real-time feedback control mechanism based on negative pressure curve deviation, the invention achieves closed-loop control during device operation, ensuring consistency between actual operating results and preset targets. Fifth, by constructing a multi-parameter linkage safety protection system, the invention improves the safety of bloodletting cupping operations. Sixth, by using a parameter recording and knowledge base update mechanism, the invention enables continuous system optimization and knowledge accumulation. Attached Figure Description

[0011] Figure 1 This is a flowchart of the intelligent control method for the operating parameters of the integrated acupoint bloodletting and cupping device of the present invention.

[0012] Figure 2 This is a diagram of the architecture of the intelligent control system for the operating parameters of the integrated acupoint bloodletting and cupping device of the present invention. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operating procedures; however, the scope of protection of the present invention is not limited to the following embodiments.

[0014] Reference Figure 1As shown, the intelligent control method for operating parameters of the integrated acupuncture and cupping device provided by this invention includes a user information input step S1, a site feature analysis step S2, a parameter knowledge base retrieval step S3, a parameter intelligent matching step S4, a real-time feedback control step S5, a safety protection step S6, and a parameter recording step S7. These seven steps form a deeply coupled closed-loop collaborative architecture, where the output of the preceding steps serves as the key input for the subsequent steps, and the execution results of the subsequent steps can inversely influence the parameter optimization of the preceding steps, thereby achieving a non-linear improvement in overall control efficiency.

[0015] Step S1: User information input step.

[0016] The user information input step is the data source for the entire control method, responsible for obtaining the user's basic information about the target user. In one embodiment of the present invention, the user's basic information covers four core dimensions: age information, body type, skin thickness measurement value, and operation site selection.

[0017] Regarding the acquisition and processing of age information, the system allows operators to directly input the age value of the user through a human-computer interaction interface. Considering the significant differences in skin tissue characteristics among different age groups, this invention divides age information into multiple intervals for processing. Preferably, the age intervals are divided as follows: infant group (0-3 years), children group (4-12 years), adolescent group (13-17 years), young adult group (18-40 years), middle-aged group (41-60 years), and elderly group (60 years and above). Different age groups correspond to different age factors. Among them, the infant group A value of 0.7 indicates that the skin tissue in this age group is relatively delicate, requiring a corresponding reduction in acupuncture depth and negative pressure intensity; Youth group The baseline value is 1.0; elderly group The value was set at 0.85, taking into account the characteristics of decreased skin elasticity and weakened tissue repair ability in the elderly.

[0018] Regarding the determination of body constitution type, this invention adopts the TCM constitution identification standard, classifying body constitution types into nine basic types: balanced constitution, qi deficiency constitution, yang deficiency constitution, yin deficiency constitution, phlegm-dampness constitution, damp-heat constitution, blood stasis constitution, qi stagnation constitution, and special constitution. Each constitution type corresponds to different constitution factors. In one embodiment of the present invention, the peaceful quality A value of 1.0 is used as the baseline reference; Qi deficiency constitution and Yang deficiency constitution A value of 0.85 indicates that these two types of people have insufficient vital energy and relatively low tolerance to external stimuli; phlegm-dampness constitution. A value of 1.1 is used, indicating that people with this constitution have relatively abundant subcutaneous adipose tissue, and the parameter intensity can be appropriately increased; Blood stasis constitution A value of 1.05 is suitable for enhancing the effects of bloodletting to promote blood circulation. The determination of body constitution type can be made by the operator based on information from the four diagnostic methods (inspection, auscultation and olfaction), or by using a built-in body constitution identification questionnaire.

[0019] Regarding the acquisition of skin thickness measurements, preferably, the system is equipped with an ultrasonic skin thickness measurement module, which can measure the skin thickness of the operating area in real time. The system achieves a skin thickness measurement accuracy of 0.1 mm, covering a range from 0.5 mm to 4.0 mm. In cases where real-time measurement is not possible, the system also supports operator estimation based on experience; in this case, the system will provide reference values ​​by incorporating age and gender information. In one embodiment of the invention, the reference value for back skin thickness in adult males is 2.0-2.5 mm, and for adult females, it is 1.5-2.0 mm. Abdominal skin thickness is typically 0.3-0.5 mm thinner than back skin. Skin thickness on the limbs varies considerably depending on the location; the inner side of the upper limbs has thinner skin (reference value 1.0-1.5 mm), while the outer side of the lower limbs has thicker skin (reference value 2.5-3.0 mm).

[0020] Regarding the selection of operating sites, the system pre-sets the location information and anatomical parameters of acupoints along the major meridians of the human body. Operators can select specific operating sites on the human body model through a graphical interface, or quickly locate them by searching for acupoint names. The system's built-in acupoint database contains 361 standard acupoints and commonly used extraordinary acupoints, each acupoint being associated with the anatomical features of its corresponding location.

[0021] The output data from the user information input step includes: age value and corresponding age factor. Body type codes and corresponding body factors Skin thickness measurement value Operation site coding. This data will serve as key inputs for subsequent steps S2 (site feature analysis) and S4 (parameter intelligent matching).

[0022] Step S2: Location feature analysis step.

[0023] The site feature analysis step determines site feature parameters based on the anatomical characteristics of the operation site, providing an anatomically based scientific basis for intelligent parameter matching. This step receives the operation site code output from step S1 and extracts the corresponding site feature parameters from the built-in anatomical feature database.

[0024] This invention establishes an anatomical feature mapping model for human operating sites, based on extensive anatomical research data and clinical measurement data. The main site feature parameters include: skin thickness range. Reference values ​​for subcutaneous tissue depth Reference values ​​for muscle layer thickness Distribution characteristics of important blood vessels and nerves, and degree of proximity to bones.

[0025] For different anatomical regions, this invention establishes a detailed database of characteristic parameters. In one embodiment of this invention, the parameters for the back region are set as follows: For the upper back region (the area connecting the Dazhui acupoint to the lower angle of the scapula), the skin thickness ranges from 1.8 to 2.5 mm, the subcutaneous tissue depth reference value is 5 to 15 mm, the muscle layer thickness reference value is 10 to 30 mm, the distribution characteristics of important blood vessels and nerves are marked as moderate, and the degree of bone proximity is near (adjacent to the spine); for the middle back region (the area from the lower angle of the scapula to the twelfth rib), the skin thickness ranges from 2.0 to 2.8 mm, the subcutaneous tissue depth reference value is 8 to 20 mm, and the muscle layer thickness reference value is 15 to 35 mm; for the lower back region (lumbosacral region), the skin thickness ranges from 2.2 to 3.0 mm, and the subcutaneous tissue depth reference value is 10 to 25 mm.

[0026] The characteristic parameters for the abdominal region were set to take into account the influence of different body types. Preferably, the skin thickness in the upper abdomen (the area connecting the xiphoid process to the umbilicus) ranges from 1.5 to 2.2 mm. The reference values ​​for subcutaneous tissue depth vary significantly depending on body type: 5-10 mm for lean individuals, 10-20 mm for normal individuals, and 20-40 mm for overweight individuals. Important blood vessel and nerve distribution features in the abdominal region are marked high, requiring special attention to the course of the superficial abdominal wall arteries and veins.

[0027] The characteristic parameters for the limb regions take into account the anatomical differences in different areas. On the medial side of the upper limb (such as the area along the three Yin meridians of the hand), the skin thickness ranges from 0.8 to 1.5 mm, and the reference value for subcutaneous tissue depth is 3 to 8 mm. Important blood vessels and nerve distribution features are marked as high (adjacent to the brachial plexus and major blood vessels). On the lateral side of the upper limb, the skin thickness ranges from 1.5 to 2.2 mm, and the reference value for subcutaneous tissue depth is 5 to 12 mm. In the lower limb region, the skin thickness on the lateral thigh ranges from 2.0 to 3.0 mm, and the reference value for subcutaneous tissue depth is 10 to 30 mm; the skin thickness on the posterior side of the lower leg ranges from 1.8 to 2.5 mm.

[0028] Based on the above anatomical feature data, this invention designs a site factor. The calculation method is as follows. The location factor comprehensively reflects the suitability of bloodletting and cupping at that location and the parameter adjustment coefficients. Location Factor The calculation formula is:

[0029] ,

[0030] in: This is a location factor, dimensionless, with a value range of 0.6-1.4; As the baseline factor, the values ​​are preset according to the location zones: 1.0 for the middle back, 0.9 for the abdomen, 0.8 for the inner side of the limbs, and 1.1 for the outer side of the limbs. The safety correction factor is determined based on the distribution characteristics of important blood vessels and nerves. The value is 1.0 when the distribution characteristics are low, 0.95 when the distribution characteristics are medium, and 0.85 when the distribution characteristics are high. This is a tissue thickness correction factor, based on the reference value of subcutaneous tissue depth. Calculate, when When mm, the value is 1.1. When mm, the value is 1.0. The value is 0.9 when it is in mm.

[0031] In one embodiment of the present invention, it is assumed that the operation site is the Feishu acupoint in the middle of the back. The anatomical characteristics of this acupoint are as follows: skin thickness ranges from 2.0 to 2.5 mm, subcutaneous tissue depth reference value is 12 mm, the distribution characteristics of important blood vessels and nerves are moderate, and the degree of proximity to bones is near. According to the above calculation formula, the reference site factor... Safety correction factor Tissue thickness correction factor Final location factor .

[0032] The output data of the site feature analysis step includes: skin thickness range, subcutaneous tissue depth reference value, and site factors. Safety warning signs (set when near important blood vessels, nerves, or bones). This data will be passed to step S3 for parameter knowledge base retrieval and step S4 for intelligent parameter matching.

[0033] Step S3: Parameter knowledge base retrieval step.

[0034] The parameter knowledge base retrieval step retrieves recommended parameter ranges that match the user's basic information and site-specific characteristics from the parameter knowledge base. The parameter knowledge base is the core data resource of this invention, storing parameter recommendation information based on extensive clinical practice data and expert experience.

[0035] The parameter knowledge base uses a multi-dimensional indexing structure, with key index dimensions including: age group, body type, treatment site region, and subcutaneous tissue depth range. Each record contains the following field: recommended acupuncture depth range. Recommended range of needle puncture speed Recommended range of negative pressure intensity Recommended range of negative pressure duration Confidence score, data source labeling.

[0036] In one embodiment of the present invention, some typical records of the parameter knowledge base are as follows: For the combination of conditions for the youth group (18-40 years old), balanced constitution, mid-back region, and subcutaneous tissue depth of 10-15 mm, the recommended range for acupuncture depth is 3-8 mm, the recommended range for acupuncture speed is 2-5 mm / s, the recommended range for negative pressure intensity is -30~50 kPa, the recommended range for negative pressure duration is 5-10 min, and the confidence score is 0.92. For the combination of conditions for the elderly group (over 60 years old), Qi deficiency constitution, upper abdominal region, and subcutaneous tissue depth of 15-25 mm, the recommended range for acupuncture depth is 2-5 mm, the recommended range for acupuncture speed is 1-3 mm / s, the recommended range for negative pressure intensity is -20~35 kPa, the recommended range for negative pressure duration is 8-15 min, and the confidence score is 0.88.

[0037] The parameter knowledge base retrieval employs a multi-level matching strategy. First, exact matching is performed; if all index dimensions match completely, the corresponding record is returned directly. When exact matching fails, fuzzy matching is enabled, allowing extended matching of adjacent intervals for age groups and subcutaneous tissue depth ranges, and reducing the confidence score based on the degree of matching. If fuzzy matching still cannot find a suitable record, a default parameter strategy is activated, returning a generally recommended parameter range for the operation site, and setting the confidence score to a lower value to alert the operator.

[0038] In one embodiment of the present invention, the specific process of the retrieval algorithm is as follows: First, based on the age factor obtained in step S1... The process involves determining the age group index, the body type index, the site partition index based on the operation site code obtained in step S2, and the depth range index based on the subcutaneous tissue depth reference value. A multidimensional retrieval condition vector is then constructed and searched in the parameter knowledge base. For the multiple candidate records returned by the retrieval, they are sorted according to their confidence scores, and the record with the highest score is selected as the retrieval result. Finally, the retrieval results are validated to ensure the logical rationality of each parameter range.

[0039] The parameter knowledge base also has dynamic update capabilities, enabling continuous optimization of recommended parameter ranges based on the actual operational data and clinical feedback collected in step S7. Preferably, when the cumulative number of operational records for a certain condition combination exceeds a set threshold (e.g., 100 times) and the feedback effect is evaluated as good, the system automatically triggers a statistical update of the parameter range and adjusts the confidence score accordingly.

[0040] The output data of the parameter knowledge base retrieval step includes: recommended intervals for acupuncture depth, acupuncture speed, negative pressure intensity, and negative pressure duration, as well as a retrieval confidence score. This data will serve as the input constraints for the intelligent parameter matching in step S4.

[0041] Step S4: Intelligent parameter matching step.

[0042] The intelligent parameter matching step is the core innovative module of this invention. It employs a method combining fuzzy inference and machine learning models to integrate user basic information and site feature parameters to output the final combination of operating parameters. This step receives the user basic information from step S1, the site feature parameters from step S2, and the recommended parameter range from step S3 as inputs. After processing by the intelligent matching algorithm, it outputs the acupuncture depth. Needle speed negative pressure strength Duration of negative pressure Four core operating parameters.

[0043] The fuzzy-machine learning joint inference framework designed in this invention includes two parallel processing paths: a fuzzy inference path and a machine learning prediction path. The outputs of the two paths are weighted and fused to obtain the final parameter values.

[0044] Regarding the design of the fuzzy inference path, this invention employs a Mamdani-type fuzzy inference system. The input variables of the fuzzy inference system include an age factor. Body constitution factors Location factor Reference values ​​for subcutaneous tissue depth Four variables. Each input variable has a corresponding fuzzy linguistic variable and membership function.

[0045] In one embodiment of the present invention, the age factor The fuzzy linguistic variables are defined as: Low (L), Low-Medium (ML), Medium (M), High-Medium (MH), and High (H), with trigonometric membership functions used for the corresponding membership degrees. (Age factor membership function) The specific definition is: exist The interval takes non-zero values, and the peak value is located at ; exist The interval takes non-zero values, and the peak value is located at ; exist The interval takes non-zero values, and the peak value is located at .

[0046] The output variable is the needle penetration depth adjustment coefficient. Needle speed adjustment coefficient Negative pressure intensity adjustment coefficient Negative pressure time adjustment coefficient Each output variable also defines five fuzzy language levels.

[0047] The fuzzy rule base contains inference rules constructed based on clinical expert knowledge. In one embodiment of the present invention, typical rules include: Rule R1: If the age factor is low and the physical condition factor is low, then the acupuncture depth adjustment coefficient is low and the negative pressure intensity adjustment coefficient is low; Rule R2: If the location factor is high and the subcutaneous tissue depth is high, then the acupuncture depth adjustment coefficient is medium-high and the negative pressure intensity adjustment coefficient is medium; Rule R3: If the age factor is medium, the physical condition factor is medium-high, and the location factor is medium, then the adjustment coefficients for each parameter are all medium. The rule base contains a total of 81 rules, covering various combinations of input variables.

[0048] The defuzzification of fuzzy inference uses the central averaging method, and the formula for calculating the sharp value of the output variable is as follows:

[0049] ,

[0050] in: This is to output the clear values ​​of the variables, that is, the final values ​​of the adjustment coefficients for each parameter; The number of activated rules represents the total number of fuzzy rules that meet the triggering conditions; For the first The activation strength of a rule is determined by the minimum membership degree of all its antecedents, and its value range is [value range missing]. ; For the first The center value of the fuzzy set output by each rule is determined according to the definition of the rule consequent. This formula achieves the conversion of fuzzy inference results into precise numerical values ​​by weighted averaging of the outputs of each activation rule.

[0051] Regarding the design of the machine learning prediction path, this invention employs a regression model based on Gradient Boosting Decision Tree (GBDT). The input feature vector of the machine learning model includes: age value, one-hot encoding of body type (9 dimensions), skin thickness measurement, one-hot encoding of the operation site (20 dimensions), subcutaneous tissue depth reference value, and statistical features of historical operation data (such as the user's historical average parameters, the site's historical average parameters, etc.). The model output is the predicted values ​​of the four operation parameters.

[0052] The training data for the machine learning model comes from the historical records of the parameter knowledge base and the continuous accumulation of parameter recording steps in step S7. Preferably, the model adopts an incremental learning strategy, triggering model updates after accumulating a certain amount of new data to keep the predictive ability synchronized with the latest clinical practice. The model training uses five-fold cross-validation to ensure generalization performance. In one embodiment of the present invention, after learning from 10,000 training samples, the model's prediction error (RMSE) on the test set is as follows: acupuncture depth 0.8 mm, acupuncture speed 0.3 mm / s, negative pressure intensity 2.5 kPa, and negative pressure duration 0.6 min.

[0053] The fusion of fuzzy inference paths and machine learning prediction paths employs a dynamic weighting strategy. The fusion formula is:

[0054] ,

[0055] in: For the final operating parameter values, including needle penetration depth Needle speed negative pressure strength Duration of negative pressure ; The output parameter values ​​for the fuzzy inference path are calculated by multiplying the adjustment coefficient by the median of the recommendation interval. The output parameter values ​​for the machine learning prediction path are directly predicted by the GBDT model. The weight coefficients for the fuzzy inference path have a range of values. ; These are the weight coefficients for the machine learning prediction path, with values ​​ranging from... And satisfy .

[0056] Weighting coefficient and The dynamic adjustment strategy is as follows: When the confidence score in step S3 is higher than 0.9 and the number of training samples for the machine learning model is less than 1000, set... , At this point, fuzzy reasoning based on expert experience is more trusted; when the number of training samples for the machine learning model exceeds 5000 and the model validation error is below a set threshold, [the following is set:] , In this case, we place more trust in data-driven machine learning predictions; in other cases, we set... , Both paths contribute equally.

[0057] The final output combination of operating parameters must meet the recommended parameter range constraints provided in step S3. If the fusion calculation result exceeds the recommended range, the parameter values ​​will be truncated to the range boundary, and a parameter adjustment prompt message will be generated.

[0058] In one embodiment of the present invention, for a scenario involving an age of 35 years, a balanced constitution, and the operation of the Lung Shu acupoint on the back, the processing procedure in step S4 is as follows: The input data is the age factor. Body constitution factors Location factor Reference values ​​for subcutaneous tissue depth mm, recommended parameter range (needle depth 3-8mm, needle speed 2-5mm / s, negative pressure intensity -30~-50kPa, negative pressure duration 5-10min). The fuzzy inference path output adjustment coefficients are all close to 1.0, resulting in a calculated needle depth of 5.3mm, needle speed of 3.4mm / s, negative pressure intensity -39kPa, and negative pressure duration of 7.4min. The machine learning prediction path outputs a needle depth of 5.5mm, needle speed of 3.2mm / s, negative pressure intensity -41kPa, and negative pressure duration of 7.8min. Using... , The weighted fusion results in the final output operating parameter combination as follows: needle depth 5.4mm, needle speed 3.3mm / s, negative pressure intensity -40kPa, and negative pressure duration 7.6min.

[0059] Step S5: Real-time feedback control steps.

[0060] The real-time feedback control step collects data from the negative pressure sensor and displacement sensor during device operation. Based on the deviation between the actual negative pressure curve and the preset target, it dynamically adjusts the negative pressure pump power to achieve closed-loop control. This step is crucial for ensuring that the device's operating performance matches the preset target.

[0061] The real-time feedback control mechanism designed in this invention includes four functional modules: sensor data acquisition, target curve generation, deviation calculation, and controller output. The sensor data acquisition module collects real-time data from the negative pressure sensor and displacement sensor at a fixed sampling period (preferably 50ms). The negative pressure sensor measures the negative pressure value within the cupping cavity. The measurement range is 0 to -100 kPa, and the measurement accuracy is 0.5 kPa. The displacement sensor measures the actual displacement of the needle-punching mechanism. The measurement range is 0-30mm, and the measurement accuracy is 0.1mm.

[0062] The target curve generation module generates a preset negative pressure establishment curve and acupuncture displacement curve based on the combination of operating parameters output in step S4. In one embodiment of the present invention, the negative pressure establishment target curve adopts an S-shaped curve model to avoid discomfort to skin tissue caused by sudden changes in negative pressure. The mathematical expression of the S-shaped negative pressure target curve is:

[0063] ,

[0064] in: for The target negative pressure value at any given time, in kPa; The final negative pressure intensity parameter output in step S4 is in kPa and ranges from -10 to -80 kPa. This is the curve steepness coefficient, which controls the speed at which negative pressure is established. It is dimensionless, and the preferred value range is 0.5-2.0. The larger the value, the faster the negative pressure is established. The inflection point of the curve, i.e., the time when the negative pressure reaches half of the target value, is measured in seconds and is usually set to half of the total time of the negative pressure establishment phase. The value is the current time, expressed in seconds. This S-shaped curve ensures that the negative pressure rises slowly in the initial stage, establishes rapidly in the middle stage, and gradually stabilizes as it approaches the target value.

[0065] The deviation calculation module calculates the deviation between the actual negative pressure value and the target negative pressure value in real time. (Negative pressure deviation) The calculation formula is:

[0066] ,

[0067] in: for The negative pressure deviation at any given time is expressed in kPa. A positive value indicates that the actual absolute value of the negative pressure is less than the target value (insufficient suction), while a negative value indicates that the actual absolute value of the negative pressure is greater than the target value (excessive suction). for The target negative pressure value at any given time is calculated by the target curve generation module. for The actual negative pressure measurement value at any given time is obtained by the negative pressure sensor.

[0068] The controller output module employs an improved PID control algorithm to calculate the power adjustment of the negative pressure pump based on the negative pressure deviation. The improved PID controller designed in this invention introduces a variable gain mechanism and an anti-integral saturation strategy to adapt to the control requirements of different operating stages. Power adjustment amount. The calculation formula is:

[0069] ,

[0070] in: for The amount of negative pressure pump power adjustment at any given time, in W; This is a time-varying proportional coefficient that adaptively adjusts according to the magnitude of the deviation. kPa To expedite the response, when kPa To reduce overshoot, other situations ; This is the integral coefficient, preferably ranging from 0.01 to 0.5, used to eliminate steady-state errors; This is the differential coefficient, preferably ranging from 0.001 to 0.1, used to suppress oscillations caused by excessively large deviation rates of change; The time integral of the deviation is used to accumulate and eliminate persistent deviations; This represents the rate of change of the deviation, used to predict deviation trends and make adjustments in advance.

[0071] The anti-integral saturation strategy is implemented by setting upper and lower thresholds for the integral term. When the integral term exceeds the threshold range, integration accumulation stops to prevent the controller output from saturating for an extended period. Preferably, the upper limit of the integral term is set to 20 W·s, and the lower limit is set to -20 W·s.

[0072] In one embodiment of the present invention, during the initial stage of negative pressure establishment, the target negative pressure is set to -40 kPa, and the actual measured negative pressure is -25 kPa, with a deviation of... kPa. At this time Assuming , The cumulative integral term is -50 kPa·s, and the rate of change of deviation is -2 kPa / s. The calculated power adjustment is... W. This negative value indicates that the power of the negative pressure pump needs to be increased (the greater the power, the greater the absolute value of the negative pressure; the negative pressure is expressed as a negative value in the deviation expression). After continuous adjustment by the controller, the actual negative pressure value gradually approaches the target value, and the deviation is controlled within ±1 kPa in the steady state stage.

[0073] The displacement control during the acupuncture process employs a similar feedback adjustment mechanism. Based on the deviation between the actual acupuncture depth measured by the displacement sensor and the target depth, the motor speed or stepping pulse frequency of the acupuncture drive mechanism is adjusted to ensure that the acupuncture depth reaches the target value set in step S4. The accuracy is controlled within ±0.2mm.

[0074] Step S6: Safety protection steps.

[0075] The safety protection procedure sets upper and lower threshold values ​​for each parameter and continuously monitors the device's operating status. When any parameter exceeds the upper or lower threshold, a protective action is automatically executed. This step is the last line of defense to ensure the safety of the bloodletting cupping procedure.

[0076] The multi-parameter linkage safety protection mechanism designed in this invention includes three functional modules: parameter threshold setting, real-time monitoring and judgment, and protection action execution.

[0077] The parameter threshold setting module dynamically calculates the safe upper and lower limits of each operating parameter based on the characteristics of the object being operated on and the features of the operating site. The threshold setting comprehensively considers the results of the site feature analysis in step S2 and the requirements of medical safety regulations. In one embodiment of the present invention, the threshold setting rules for each parameter are as follows:

[0078] Upper limit threshold of needle penetration The calculation formula is:

[0079] ,

[0080] in: This is the upper limit threshold for needle puncture depth, in mm. Exceeding this value will trigger safety protection. The skin thickness measurement value obtained in step S1, in mm; The reference value for subcutaneous tissue depth obtained in step S2 is in mm; For safety factors, dimensionless, the value ranges from 0.5 to 0.8. For areas marked as high in the distribution characteristics of important blood vessels and nerves, a value of 0.5 is used; for areas marked as medium, a value of 0.6 is used; and for areas marked as low, a value of 0.8 is used. This is the absolute upper limit of depth, set according to the treatment site: 20mm for the back, 15mm for the abdomen, and 12mm for the limbs. This formula ensures that the acupuncture depth will not exceed the safe depth of the subcutaneous tissue, avoiding damage to deeper tissue structures.

[0081] Lower limit threshold of needle penetration depth The fixed setting is 0.5mm; below this value, no effective stimulation can be generated.

[0082] upper limit threshold of negative pressure intensity (Upper limit of absolute value) is set differently based on the age and physical condition of the user. Young, healthy individuals. Set to -70kPa, for the elderly population Set to -50kPa, for infants and young children Set to -25 kPa, individuals with sensitive constitutions such as those with Qi deficiency should further reduce the pressure by 10 kPa from the corresponding age group level. Lower limit threshold for negative pressure intensity. The lower absolute limit is set to -10 kPa; below this value, effective adsorption cannot be formed.

[0083] upper limit threshold of negative pressure duration The time limit is set to 25 minutes; exceeding this time may cause skin damage or blister formation. For infants and the elderly, the upper limit is further reduced to 15 minutes.

[0084] Needle speed upper limit threshold Setting the speed to 10 mm / s is recommended, as excessively fast needle insertion speeds may lead to tissue tearing or loss of control.

[0085] The real-time monitoring and judgment module samples and compares thresholds for each operating parameter at a fixed period (preferably 10ms). The monitoring and judgment logic adopts a multi-parameter linkage mechanism, meaning that either a single parameter exceeding its limit or an abnormal parameter combination can trigger protection. The judgment rules for abnormal parameter combinations include: when the needle penetration depth exceeds 120% of the target value and the absolute value of the negative pressure exceeds 110% of the target value, it is determined to be an abnormal parameter combination; when the rate of change of the displacement sensor reading exceeds 15mm / s (far greater than the set needle penetration speed), it is determined to be abnormal movement of the mechanism.

[0086] Upon detecting an anomaly, the protection action execution module immediately executes a protection action sequence. This sequence includes: first, stopping the needle drive mechanism and locking the needle motor; then, activating the negative pressure release valve to rapidly release the negative pressure within the cupping chamber, controlling the release rate at 5 kPa / s to avoid sudden skin discomfort; simultaneously, cutting off the power to the negative pressure pump and shutting down the negative pressure building system; and finally, triggering an audible and visual alarm and displaying the reason for the protection trigger and details of the parameter exceeding limits on the human-machine interface.

[0087] Preferably, the safety protection steps also include an early warning mechanism. When the parameter is close to the threshold (e.g., reaches 90% of the threshold) but has not exceeded the limit, the system issues an early warning to remind the operator to pay attention to the parameter status and to proactively adjust or prepare countermeasures.

[0088] Step S7: Parameter recording steps.

[0089] The parameter recording step saves the parameters from each run for subsequent analysis and optimization. This step is a key step in enabling the system to continuously learn and accumulate knowledge.

[0090] The parameter recording mechanism designed in this invention includes three functional modules: data acquisition, data storage, and data analysis. The data acquisition module records the following information throughout the entire operation of the device: operation timestamp, operation object identifier (after anonymization), user basic information summary, location characteristic parameters, parameter knowledge base retrieval results, operating parameter combinations output by intelligent parameter matching, parameter fluctuation records during real-time feedback control, safety protection trigger records (if any), and effect evaluation feedback after the operation ends.

[0091] The data storage module stores the collected data in a structured format in a local database and supports periodic uploads to a cloud server for aggregation and analysis (provided user authorization is obtained). Data storage utilizes a time-series database to facilitate subsequent trend analysis and pattern discovery.

[0092] The data analysis module performs statistical analysis and machine learning model training on the accumulated operational data. Preferably, the data analysis includes the following: correlation analysis between parameter settings and effect evaluation to discover the optimal parameter configuration pattern; applicability assessment of parameter ranges to optimize the recommended range of the parameter knowledge base; incremental training of the machine learning model to improve the prediction accuracy of intelligent parameter matching in step S4; and anomaly pattern recognition to discover potential equipment failures or operational risks.

[0093] The output data from the parameter recording step is fed back to the parameter knowledge base in step S3 and the machine learning model in step S4, forming a closed-loop optimization mechanism. In one embodiment of the present invention, after the system has run for 6 months and accumulated 5,000 running records, the recommended parameter range of the parameter knowledge base has narrowed by an average of 15% (indicating more accurate recommendations), the prediction error of the machine learning model has decreased by 22%, and the overall parameter matching satisfaction has increased from the initial 85% to 93%.

[0094] Reference Figure 2 As shown, the present invention also provides an intelligent control system for the operating parameters of an integrated acupuncture and cupping device. This system is used to execute the control method described in the above-described method embodiments. The system includes a user information input module 1, a site feature analysis module 2, a parameter knowledge base module 3, a parameter intelligent matching module 4, a real-time feedback control module 5, a safety protection module 6, and a parameter recording module 7. Each module has a one-to-one correspondence with the steps in the method embodiments.

[0095] User information input module 1 corresponds to step S1 in the method embodiment and is used to obtain basic user information of the operation object. This module includes a human-computer interaction unit and a skin thickness measurement unit. The human-computer interaction unit provides a touchscreen display interface, supporting the operator to input or select information such as age, body type, and operation site. The interface design conforms to medical device human factors engineering specifications. The skin thickness measurement unit integrates an ultrasonic measurement probe, which can quickly and accurately measure the skin thickness of the operation site. Preferably, the human-computer interaction unit also provides a voice input function, facilitating information entry by the operator when they cannot touch the screen.

[0096] The site feature analysis module 2 corresponds to step S2 in the method embodiment and is used to determine site feature parameters based on the anatomical features of the operation site. This module includes an anatomical feature database and a site factor calculation unit. The anatomical feature database stores information such as the skin thickness range, subcutaneous tissue depth reference values, muscle layer thickness reference values, and distribution characteristics of important blood vessels and nerves for various parts of the human body. The site factor calculation unit calculates site factors according to the formula described in the method embodiment. The database supports content expansion and parameter calibration via software updates.

[0097] The parameter knowledge base module 3 corresponds to step S3 in the method embodiment and is used to store and retrieve recommended parameter ranges under different conditions. This module includes a knowledge base storage unit and a knowledge base retrieval unit. The knowledge base storage unit uses a relational database management system to store parameter recommendation records with a multi-dimensional index. The knowledge base retrieval unit implements a multi-level matching retrieval algorithm, supporting exact matching, fuzzy matching, and default parameter strategies. In one embodiment of the present invention, the parameter knowledge base initially contains 2000 basic records, covering combinations of common age groups, body types, and operating sites.

[0098] The parameter intelligent matching module 4 corresponds to step S4 in the method embodiment, and is used to output the combination of running parameters using a method combining fuzzy inference and a machine learning model. This module includes a fuzzy inference engine and a machine learning prediction engine. The fuzzy inference engine implements a Mamdani-type fuzzy inference system, including a fuzzification interface, a rule base, an inference engine, and a defuzzification interface. The machine learning prediction engine deploys a gradient boosting decision tree model, supporting online inference and offline incremental training. The outputs of the two engines are integrated through a dynamic weighted fusion unit to output the final combination of running parameters.

[0099] The real-time feedback control module 5 corresponds to step S5 in the method embodiment, and is used to dynamically adjust the negative pressure pump power based on the deviation between the actual value and the preset target. This module includes a sensor interface unit, a target curve generation unit, and a closed-loop controller unit. The sensor interface unit connects a negative pressure sensor and a displacement sensor, acquiring data at 50ms intervals. The target curve generation unit calculates the S-shaped negative pressure and establishes the target curve based on the combination of operating parameters. The closed-loop controller unit implements an improved PID control algorithm and outputs a negative pressure pump power adjustment command. Preferably, the closed-loop controller unit is implemented using a dedicated digital signal processor to ensure the real-time performance of the control loop.

[0100] Safety protection module 6 corresponds to step S6 in the method embodiment and is used to monitor various parameters and execute protective actions when limits are exceeded. This module includes a threshold calculation unit, a real-time monitoring unit, and a protection execution unit. The threshold calculation unit dynamically calculates the safety threshold based on the characteristics of the object being operated on and the location of the affected area. The real-time monitoring unit samples various operating parameters at 10ms intervals and compares the thresholds. Upon detecting an anomaly, the protection execution unit executes a sequence of protective actions, including stopping the needle puncture mechanism, activating the negative pressure release valve, cutting off the power to the negative pressure pump, and triggering audible and visual alarms. The safety protection module employs a hardware redundancy design, with key protection functions implemented by an independent safety monitoring circuit, ensuring effective execution of protection functions even if the main controller fails.

[0101] The parameter recording module 7 corresponds to step S7 in the method embodiment and is used to save operating parameters for subsequent analysis. This module includes a data acquisition unit, a data storage unit, and a data analysis unit. The data acquisition unit records parameter information and event logs throughout the entire operation. The data storage unit uses a time-series database to store structured data, supporting efficient historical data retrieval. The data analysis unit provides functions such as data statistics, trend analysis, and model training; the analysis results are used to optimize the parameter knowledge base and machine learning models.

[0102] Data interaction between the various modules of the system is achieved through an internal communication bus. In one embodiment of the present invention, the system adopts an embedded computing platform based on a real-time operating system, with the main processor being an ARM Cortex-A series processor running at a frequency of 1.5 GHz and 4 GB of memory. After power-on, the system completes a self-test and enters standby mode within approximately 10 seconds, responding to operator input and commands.

[0103] In summary, the intelligent control method and system for the operating parameters of the integrated acupuncture and cupping device provided by this invention achieves scientific, intelligent, and individualized setting and control of the operating parameters of the integrated acupuncture and cupping device through the deep coupling and closed-loop collaboration of seven steps / modules: user information input, site feature analysis, parameter knowledge base retrieval, intelligent parameter matching, real-time feedback regulation, safety protection, and parameter recording. This significantly improves the working efficiency and safety of the device.

[0104] The embodiments of the present invention are not limited to the specific embodiments described above. Those skilled in the art can make various equivalent changes or substitutions based on the technical solutions of the present invention, and all such changes or substitutions should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of operating parameters of an integrated acupoint bloodletting and cupping device, characterized in that, Includes the following steps: Perform the user information input step to obtain the user's basic information, including age, body type, skin thickness and operation site. Divide age groups according to age and determine age factors, and determine body factors according to body type. The procedure involves performing site feature analysis, determining site feature parameters based on the anatomical features of the operation site, including the skin thickness range and subcutaneous tissue depth reference values, and calculating the site factor based on the baseline site factor, safety correction coefficient, and tissue thickness correction coefficient. Perform the parameter knowledge base retrieval step, retrieve recommended parameter ranges that match the user's basic information and site feature parameters from the parameter knowledge base. The parameter knowledge base stores recommended ranges for acupuncture depth and negative pressure intensity under different site conditions and different physical conditions. The execution parameter intelligent matching step adopts a method combining fuzzy inference and machine learning model. The age factor, physical condition factor, location factor and subcutaneous tissue depth reference value are used as input. The fuzzy inference path and machine learning prediction path are processed in parallel and weighted and fused to output the combination of running parameters, which includes acupuncture depth, acupuncture speed, negative pressure intensity and negative pressure duration. The system performs real-time feedback control steps, collects data from negative pressure sensors and displacement sensors during device operation, establishes a curve based on the deviation between the actual negative pressure and the preset target, and dynamically adjusts the power of the negative pressure pump using an improved PID control algorithm. The system executes safety protection procedures, dynamically calculates the upper and lower limits of each parameter based on the characteristics of the object being operated and the features of the operating part, monitors the operating parameters in real time, and automatically stops and releases negative pressure when any parameter exceeds the upper or lower limit. The execution parameter recording step saves the parameters and effect evaluation feedback for each run, which is used for updating the parameter knowledge base and optimizing machine learning models.

2. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the user information input step, the age factor ranges from 0.7 to 1.3, with the age factor for infants and toddlers being 0.7, the age factor for young adults being 1.0, and the age factor for the elderly being 0.85; the constitution factor ranges from 0.8 to 1.2, with the constitution factor for balanced constitution being 1.0, the constitution factor for qi deficiency and yang deficiency being 0.85, and the constitution factor for phlegm-dampness being 1.

1.

3. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the site feature analysis step, the site factor ranges from 0.6 to 1.

4. The safety correction coefficient is determined based on the distribution characteristics of important blood vessels and nerves, with a value of 1.0 when the distribution characteristics are low, 0.95 when the distribution characteristics are medium, and 0.85 when the distribution characteristics are high. The tissue thickness correction coefficient is determined based on the subcutaneous tissue depth reference value, with a value of 1.1 when the subcutaneous tissue depth reference value is greater than or equal to 15 mm, a value of 1.0 when the subcutaneous tissue depth reference value is between 10 mm and 15 mm, and a value of 0.9 when the subcutaneous tissue depth reference value is less than 10 mm.

4. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the parameter knowledge base retrieval step, the recommended range for acupuncture depth is 0.5mm to 25mm, the recommended range for acupuncture speed is 0.5mm / s to 10mm / s, the recommended range for negative pressure intensity is -10kPa to -80kPa, and the recommended range for negative pressure duration is 3min to 20min.

5. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the parameter intelligent matching step, the fuzzy inference path adopts the Mamdani-type fuzzy inference system, the fuzzy rule base contains inference rules constructed based on clinical expert knowledge, and the defuzzification adopts the center average method. The machine learning prediction path uses a gradient boosting decision tree regression model. The input feature vector includes age value, body type unique heat encoding, skin thickness measurement, operation site unique heat encoding, and subcutaneous tissue depth reference value.

6. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the parameter intelligent matching step, the fusion of fuzzy inference path and machine learning prediction path adopts a dynamic weighting strategy. When the retrieval confidence score is higher than 0.9 and the number of training samples of the machine learning model is less than 1000, the weight coefficient of fuzzy inference path is set to 0.7 and the weight coefficient of machine learning prediction path is set to 0.

3. When the number of training samples of machine learning model exceeds 5000 and the model validation error is lower than the set threshold, the weight coefficient of fuzzy inference path is set to 0.4 and the weight coefficient of machine learning prediction path is set to 0.

6.

7. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the real-time feedback control step, the negative pressure target curve is established using an S-shaped curve model. The improved PID control algorithm introduces a variable gain mechanism. When the absolute value of the negative pressure deviation is greater than 5 kPa, the proportional coefficient is set to 1.

5. When the absolute value of the negative pressure deviation is less than or equal to 2 kPa, the proportional coefficient is set to 0.

8. The upper and lower limit thresholds of the integral term are set to achieve an anti-integral saturation strategy.

8. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, In the aforementioned safety protection steps, the upper limit threshold for needle puncture depth is calculated based on skin thickness, reference values ​​for subcutaneous tissue depth, and a safety factor. The safety factor ranges from 0.5 to 0.

8. The safety factor for sites marked as having high distribution characteristics of important blood vessels and nerves is 0.5, for sites marked as having medium distribution characteristics is 0.6, and for sites marked as having low distribution characteristics is 0.

8.

9. The intelligent control method for operating parameters of the integrated acupoint bloodletting and cupping device according to claim 1, characterized in that, The safety protection steps include the following action sequence: stopping the needle drive mechanism and locking it, activating the negative pressure release valve to release negative pressure at a rate of 5 kPa / s, cutting off the power supply to the negative pressure pump, triggering an audible and visual alarm and displaying the reason for the protection trigger on the human-machine interface.

10. An intelligent control system for the operating parameters of an integrated acupoint bloodletting and cupping device, characterized in that, For performing the method as described in any one of claims 1 to 9, comprising: The user information input module includes a human-computer interaction unit and a skin thickness measurement unit, which are used to obtain the user's basic information and determine the age factor and body constitution factor of the operation object; The site feature analysis module includes an anatomical feature database and a site factor calculation unit, which is used to determine site feature parameters and site factors based on the anatomical features of the operation site. The parameter knowledge base module includes a knowledge base storage unit and a knowledge base retrieval unit, which are used to store and retrieve recommended parameter ranges under different conditions; The parameter intelligent matching module, including a fuzzy inference engine and a machine learning prediction engine, is used to output a combination of running parameters by combining fuzzy inference and machine learning models. The real-time feedback control module, including a sensor interface unit, a target curve generation unit, and a closed-loop controller unit, is used to dynamically adjust the power of the negative pressure pump based on the deviation between the actual value and the preset target. The safety protection module includes a threshold calculation unit, a real-time monitoring unit, and a protection execution unit, which are used to monitor various parameters and execute protection actions when limits are exceeded. The parameter recording module includes a data acquisition unit, a data storage unit, and a data analysis unit, which is used to save operating parameters for subsequent analysis and optimization.

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