A temperature adaptive regulation system for high-frequency heating steam ablation equipment

Abstract

The present invention relates to the field of medical device engineering technology, and specifically to a temperature adaptive regulation system for high-frequency heating steam ablation equipment, comprising: a high-frequency generator, a steam generation module, an ablation electrode, an impedance detection unit, an analysis module, a temperature monitoring module, a time monitoring module, a control module, and a leakage protection module; the analysis module is used to provide treatment parameters according to the patient's condition data, the temperature monitoring module is used to collect temperature data in the steam transmission pipeline in real time, the time monitoring module is used to record time information during the patient's treatment process, the control module adjusts the power of the steam generator based on the treatment parameters and temperature data, the control module controls the working time of the steam generator based on the treatment parameters and time information, and the leakage protection module is used to monitor the leakage of the steam generator line in real time. The present invention provides a reference for treatment personnel and further improves the treatment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device engineering, and in particular to a temperature adaptive regulation system for high-frequency heating steam ablation equipment. Background Art

[0002] In the medical field, precision medicine is key to improving patient outcomes and quality of life. Traditional treatment plans are often based on physician experience and standardized guidelines. However, due to individual patient variability, this approach does not always provide optimal results. With the development of information technology, electronic medical records (EMRs) have become widely used, facilitating the collection, storage, and analysis of medical data. Effectively utilizing this data to develop personalized treatment plans for specific patients has become a key area of ​​modern medical research. Steam ablation devices, as an emerging medical technology, are increasingly being used in areas such as oncology and liver disease due to their minimally invasive and effective properties. These devices use high-temperature steam to ablate diseased tissue, achieving therapeutic effects. However, the operating parameters of steam ablation devices (such as steam temperature, flow rate, and treatment time) significantly impact treatment outcomes. Different patients require different treatment parameters depending on their specific medical conditions. Therefore, determining the optimal treatment parameters for each patient's individual condition presents a challenge in clinical practice. Summary of the Invention

[0003] In view of this, the present invention addresses the deficiencies of the prior art and proposes a temperature adaptive regulation system for high-frequency heating steam ablation equipment, aiming to solve at least one of the problems raised in the above background technology.

[0004] The present invention provides a temperature adaptive regulation system for a high-frequency heating steam ablation device, comprising: a high-frequency generator, a steam generation module, an ablation electrode, and an impedance detection unit. The ablation electrode collects an impedance signal from the steam generation module, and the impedance detection unit monitors the voltage or current across the ablation electrode in real time.

[0005] It also includes: an analysis module, the analysis module is used to provide treatment parameters according to the patient's condition data;

[0006] A temperature monitoring module, which is used to collect temperature data in the steam transmission pipeline in real time;

[0007] A time monitoring module, which is used to record time information during the patient's treatment process;

[0008] a control module, the control module adjusting the power of the steam generator based on the treatment parameter and the temperature data, the control module controlling the operating time of the steam generator based on the treatment parameter and the time information, the control module being configured to receive the impedance signal, and the control module calculating the resonant frequency based on an impedance-frequency characteristic curve;

[0009] A leakage protection module is used to monitor the circuit leakage of the steam generator in real time. The leakage protection module is electrically connected to the control module, the analysis module, the temperature monitoring module, and the time monitoring module.

[0010] In some embodiments, the analysis module obtains treatment information reports of relevant patients through the electronic medical record system EMR, constructs a historical treatment case library based on the treatment information reports of the relevant patients, searches the historical treatment case library according to the patient's condition data, and finds similar treatment information reports. The analysis module gives preset treatment parameters based on the similar treatment information reports, and determines the treatment parameters based on the relationship between the patient's condition data and the similar treatment information reports.

[0011] In some embodiments, the treatment information report includes: lesion tissue type data, lesion location data, lesion extent data, medical history information data, and clinical symptom data;

[0012] The preset treatment parameters and the treatment parameters include: steam temperature parameters, steam flow parameters, and steam treatment time parameters.

[0013] In some embodiments, the temperature monitoring module collects temperature data in the steam transmission pipeline in real time through a temperature sensor, and the temperature monitoring module transmits the real-time collected temperature data in the steam transmission pipeline to the control module through a signal transmission line;

[0014] Setting a first adjustment coefficient, when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameter, the control module reduces the steam temperature parameter in the treatment parameter according to the first adjustment coefficient;

[0015] A second adjustment coefficient is set. When the temperature actually monitored by the temperature monitoring module is lower than the steam temperature in the treatment parameter, the control module increases the steam treatment time parameter in the treatment parameter according to the second adjustment coefficient.

[0016] In some embodiments, when the control module detects that the temperature data transmitted in real time by the temperature monitoring module exceeds the steam temperature in the treatment parameter, the control module reduces the power of the steam generator;

[0017] The high-frequency generator switches the frequency in real time within a frequency range of 200kHz-1MHz, detects the resonance point, and sets the lowest impedance point as the initial value;

[0018] The temperature monitoring module controls the steam generation module through the PID algorithm based on the heat resistance of the tissue to be treated, maintaining the steam temperature error of ±2°C, setting the flow rate according to the ablation area of ​​the tissue to be treated, and adjusting the opening of the solenoid valve by feedback from the flow sensor;

[0019] The impedance detection unit collects the voltage or current across the ablation electrode at a rate of 1kHz-10kHz, calculates the dynamic impedance (Z=U / I), and uses a sliding average filter or FFT to eliminate motion artifact interference;

[0020] When the impedance change rate (ΔZ / Δt) exceeds the preset value, the tissue to be treated is judged to be degenerated and frequency tracking is started. By comparing the voltage / current phase difference, if the phase difference is greater than 5°, frequency correction is triggered;

[0021] According to the impedance deviation Adjust frequency, proportional coefficient Determine the speed of response;

[0022] Aiming at minimizing impedance, iteratively approach the target frequency

[0023] .

[0024] In some embodiments, the time monitoring module uses a clock chip as a time reference, and counts the time during the treatment process through a timing circuit to obtain the time information, and the time monitoring module transmits the time information to the control module through a line.

[0025] In some embodiments, a third adjustment coefficient is set, and the control module reduces the power of the steam generator according to the third adjustment coefficient.

[0026] In some embodiments, the leakage protection module is provided with a leakage threshold, and the leakage protection module monitors the leakage of the steam generator circuit in real time through a leakage sensor;

[0027] When the leakage sensor detects that the leakage current reaches a first leakage threshold, a first-level warning is issued; when the leakage sensor detects that the leakage current reaches a second leakage threshold, a second-level warning is issued and power-off measures are taken through the control module.

[0028] In some embodiments, searching the historical treatment case database based on the patient's condition data to find similar treatment information reports includes:

[0029] The analysis module calculates the similarity between the patient's condition data and each case in the historical treatment case database using a Euclidean distance algorithm;

[0030] The analysis module is further configured to determine whether each case in the historical treatment case database is a similar treatment information report of the patient based on the relationship between the preset similarity and the actual similarity;

[0031] When the actual similarity is less than the preset similarity, it is determined that the case is not a similar treatment information report of the patient;

[0032] When the actual similarity is greater than or equal to the preset similarity, it is determined that the case is a similar treatment information report of the patient.

[0033] In some embodiments, the analysis module calculates the distance between the patient's condition data and each case data in the historical treatment case database using a Euclidean distance algorithm based on the relationship between each case data in the historical treatment case database and the patient's condition data;

[0034] The analysis module assigns a weight to each of the lesion tissue type data, lesion location data, lesion severity data, medical history information data, and clinical symptom data; the lesion tissue type data, lesion location data, lesion severity data, medical history information data, and clinical symptom data are feature vectors;

[0035] Assume that the lesion tissue type data is X1, the lesion location data is X2, the lesion extent data is X3, the medical history information data is X4, and the clinical symptom data is X5;

[0036] The patient's diseased tissue type data is X1 new , the patient's lesion location data is X2 new , the patient's lesion severity data is X3 new , the patient's medical history information data is X4 new , the patient's clinical symptom data is X5 new ;

[0037] The lesion tissue type data of the historical cases in the historical treatment case database is X1 hist The lesion location data of the historical cases in the historical treatment case database is X2 hist The lesion severity data of the historical cases in the historical treatment case database is X3 hist The medical history information data of the historical cases in the historical treatment case database is X4 hist The clinical symptom data of the historical cases in the historical treatment case database is X5 hist ;

[0038] The calculation formula of Euclidean distance is:

[0039]

[0040] Among them, a and b are the feature vectors of patients and historical cases respectively, is the weight of the i-th feature, and are the values ​​of vectors a and b on the i-th feature, respectively, and n is the total number of features.

[0041] Compared with existing technologies, the present invention offers the following advantages: the adaptive temperature control system automatically generates treatment parameters based on the patient's medical data, collecting and adjusting steam temperature, flow rate, and treatment time in real time, significantly improving treatment efficiency and accuracy. Furthermore, the system collects treatment information reports from relevant patients through the electronic medical record (EMR) system, compiling a historical treatment case database to provide reference for treatment personnel, further enhancing treatment effectiveness. The system also includes a leakage protection module that monitors the steam generator's circuit leakage in real time. When the leakage current reaches a set threshold, it issues an alarm and shuts off the power, effectively preventing electric shock accidents. The system uses a Euclidean distance algorithm to calculate the similarity between the patient's medical data and each case in the historical treatment case database, providing treatment personnel with precise treatment parameters. Furthermore, treatment personnel can assign weights to lesion tissue type data, lesion location data, lesion severity data, medical history data, and clinical symptom data based on actual conditions, ensuring treatment is more tailored to the patient's specific needs. The system collects real-time temperature data within the steam transmission pipeline and time information during treatment, enabling timely adjustments to the steam generator's power and operating time to ensure the safety and effectiveness of the treatment process. If the system detects that the temperature or time exceeds a preset range, it automatically adjusts the steam generator's power or operating time, eliminating the need for manual intervention and significantly reducing the workload on medical staff. The system's control module adjusts the steam generator's power based on treatment parameters and temperature data, and controls the steam generator's operating time based on treatment parameters and time information, making operation simple and convenient. Furthermore, the system's leakage protection module is electrically connected to the control module, analysis module, temperature monitoring module, and time monitoring module, facilitating maintenance and repair.

[0042] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

[0043] Other features and aspects of the present disclosure will become more apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A structural block diagram of a temperature adaptive regulation system for high-frequency heating steam ablation equipment provided by an embodiment of the present invention;

[0046] Figure 2 A flow chart of a temperature adaptive adjustment system for a high-frequency heating steam ablation device according to an embodiment of the present invention;

[0047] Figure 3 A flowchart of the temperature adaptive adjustment system for high-frequency heating steam ablation equipment provided by an embodiment of the present invention;

[0048] Figure 4 This is a block diagram of the steps of the temperature adaptive adjustment system for high-frequency heating steam ablation equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0051] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0052] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0053] See Figure 1-4 As shown, a temperature adaptive regulation system for a high-frequency heating steam ablation device according to an embodiment of the present application includes: a high-frequency generator, a steam generation module, an ablation electrode, and an impedance detection unit. The ablation electrode collects an impedance signal from the steam generation module, and the impedance detection unit monitors the voltage or current across the ablation electrode in real time.

[0054] It also includes: an analysis module, the analysis module is used to provide treatment parameters according to the patient's condition data;

[0055] A temperature monitoring module, which is used to collect temperature data in the steam transmission pipeline in real time;

[0056] A time monitoring module, which is used to record time information during the patient's treatment process;

[0057] a control module, the control module adjusting the power of the steam generator based on the treatment parameter and the temperature data, the control module controlling the operating time of the steam generator based on the treatment parameter and the time information, the control module being configured to receive the impedance signal, and the control module calculating the resonant frequency based on an impedance-frequency characteristic curve;

[0058] A leakage protection module is used to monitor the circuit leakage of the steam generator in real time. The leakage protection module is electrically connected to the control module, the analysis module, the temperature monitoring module, and the time monitoring module.

[0059] It should be understood that steam transmission pipes are typically made of high-temperature and corrosion-resistant materials, such as stainless steel or special alloys, to ensure they do not deform or damage in high-temperature and high-pressure environments. The inside of the pipe may be coated with a special coating to reduce heat loss and condensation during steam transmission. The pipe is designed as a multi-layer structure, including an inner layer for steam transmission, a middle layer for thermal insulation, and an outer layer to protect the pipe from the external environment. The main function of the steam transmission pipe is to transmit the generated steam from the steam generator to the treatment site. The inside of the pipe is smooth to reduce resistance and turbulence during steam flow.

[0060] The working principle of a steam generator is to release heat through a heating device (such as an electric heating element or gas burner). This heat is transferred to the surrounding water, gradually raising the water temperature and ultimately converting it into steam. During the heating process, the water is first preheated to a certain temperature and then further heated to boiling, generating steam. The generated steam can then be transported through pipelines to the required location for use.

[0061] See Figure 2 Specifically, as the system's core decision-making unit, the analysis module is responsible for integrating and processing data from the temperature monitoring module, the time monitoring module, and possibly other sensors (such as flow sensors and pressure sensors). Based on this data and pre-defined algorithms or models, it analyzes the patient's condition or treatment needs and generates corresponding treatment parameters. The temperature monitoring module monitors the temperature within the steam transmission pipeline in real time, ensuring that the temperature remains within a safe and effective range during treatment. This temperature data is transmitted to the analysis and control modules in real time for real-time adjustments. The time monitoring module accurately records the duration of the treatment process, including preheating time, treatment time, and any pauses. This time data is transmitted to the analysis and control modules to assist in adjusting treatment parameters and ensuring the timeliness of the treatment process. Based on the treatment parameters generated by the analysis module and real-time data provided by the temperature monitoring and time monitoring modules, the control module precisely controls the steam generator. It adjusts the steam generator's power, operating time, and possibly other parameters to ensure the accuracy and effectiveness of the treatment process. The leakage protection module monitors the steam generator and its related circuits for leakage in real time to ensure electrical safety. Once a leakage is detected, the alarm system is immediately triggered and emergency measures are taken, such as cutting off the power supply, initiating emergency procedures, etc. The temperature monitoring module and the time monitoring module transmit the collected data to the analysis module and the control module through high-speed communication interfaces (such as USB, Bluetooth, Wi-Fi, etc.).

[0062] In some specific embodiments, the analysis module obtains treatment information reports of relevant patients through the electronic medical record system EMR, constructs a historical treatment case library based on the treatment information reports of relevant patients, searches the historical treatment case library according to the patient's medical condition data, and finds similar treatment information reports. The analysis module gives preset treatment parameters based on similar treatment information reports, and determines the treatment parameters based on the relationship between the patient's medical condition data and similar treatment information reports.

[0063] It should be understood that the analysis module is deeply integrated with the hospital or medical institution's electronic medical record (EMR) system, enabling data interoperability and sharing through an API interface. This integration ensures real-time and accurate data. The analysis module can instantly access patient treatment information reports, including lesion tissue type, location, severity, medical history, and clinical symptom data. After acquiring the raw data, the analysis module first cleans the data to remove noise, duplication, and inconsistencies. Next, the data is standardized, such as through unified units, format conversion, and terminology standardization, to ensure consistency and comparability. The standardized data is stored in a structured database for easy subsequent analysis and retrieval. The analysis module displays preset treatment parameters to treatment personnel in the form of charts, tables, or text. These preset parameters may include recommended medications and dosages, treatment options, expected treatment effects, and potential risks. Treatment personnel will adjust these parameters based on specific circumstances. This may be based on various factors, such as the patient's specific response, the physician's judgment, and the hospital's equipment. During the adjustment process, therapists can refer to similar case details provided by the analysis module, as well as the source and basis for the preset parameters. The adjusted treatment parameters are then more closely aligned with the patient's actual situation and needs. These adjusted treatment parameters are confirmed and entered into the system to guide the actual treatment process. At this point, therapists can develop a detailed treatment plan, including medication prescriptions, treatment schedules, and follow-up plans. The system also records these confirmed treatment parameters for future tracking and evaluation.

[0064] Specifically, by searching the historical treatment case database, the analysis module can identify historical cases with similar conditions to the current patient and, based on these cases, generate preset treatment parameters. This allows treatment plans to be more tailored to the patient's actual condition, improving treatment precision. Preset treatment parameters provide a crucial reference point for therapists, helping them better understand how to adjust treatment parameters to optimize outcomes. Therapists can fine-tune the preset parameters based on their specific circumstances, enabling more personalized treatment. With preset treatment parameters as a starting point, therapists can more quickly develop preliminary treatment plans, eliminating the need to consider all possible treatment options from scratch. This significantly shortens treatment planning time and improves treatment efficiency. In urgent or complex cases, therapists may face significant decision-making pressure. Preset treatment parameters provide a clear starting point, helping to reduce decision burden and fatigue. Preset treatment parameters are generated based on data from the historical treatment case database and are highly reliable. By relying on objective historical data and algorithm-derived preset parameters, therapists can mitigate the influence of personal bias on treatment plans. Over time, the historical treatment case database continuously accumulates new cases and treatment experience. This valuable knowledge and experience can be passed on and shared through the analysis module, providing better treatment options for future patients. The analysis module also promotes collaboration and knowledge sharing between different departments. By integrating the treatment experience and data of different departments, more comprehensive and integrated treatment plans can be formed.

[0065] In some specific embodiments, the treatment information report includes: lesion tissue type data, lesion location data, lesion extent data, medical history information data, and clinical symptom data;

[0066] The preset treatment parameters and treatment parameters include: steam temperature parameters, steam flow parameters, and steam treatment time parameters.

[0067] In some specific embodiments, the temperature monitoring module collects temperature data in the steam transmission pipeline in real time through a temperature sensor, and the temperature monitoring module transmits the real-time collected temperature data in the steam transmission pipeline to the control module through a signal transmission line;

[0068] A first adjustment coefficient is set, and when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameter, the control module reduces the steam temperature parameter in the treatment parameter according to the first adjustment coefficient;

[0069] A second adjustment coefficient is set. When the temperature actually monitored by the temperature monitoring module is lower than the steam temperature in the treatment parameters, the control module increases the steam treatment time parameter in the treatment parameters according to the second adjustment coefficient.

[0070] See Figure 3As shown, specifically, when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameters, the control module will reduce the steam temperature parameter in the treatment parameters according to the first adjustment coefficient. This means that if the temperature actually monitored is higher than the preset treatment temperature, the system will automatically reduce the power of the steam generator or adjust other relevant parameters to ensure that the temperature during the treatment process does not exceed the safe range. This adjustment helps to prevent tissue overheating damage and ensure the safety of treatment. When the temperature actually monitored by the temperature monitoring module is lower than the steam temperature in the treatment parameters, the control module will increase the steam treatment time parameter in the treatment parameters according to the second adjustment coefficient. This means that if the temperature actually monitored is lower than the preset treatment temperature, the system will automatically extend the working time of the steam generator or adjust other relevant parameters to ensure that the temperature during the treatment process can achieve the expected treatment effect. This adjustment helps to improve the treatment effect and ensure that the diseased tissue is fully ablated.

[0071] In some specific embodiments, when the control module detects that the temperature data transmitted in real time by the temperature monitoring module exceeds the steam temperature in the treatment parameter, the control module reduces the power of the steam generator;

[0072] The high-frequency generator switches the frequency in real time within a frequency range of 200kHz-1MHz, detects the resonance point, and sets the lowest impedance point as the initial value;

[0073] The temperature monitoring module controls the steam generation module through the PID algorithm based on the heat resistance of the tissue to be treated, maintaining the steam temperature error of ±2°C, setting the flow rate according to the ablation area of ​​the tissue to be treated, and adjusting the opening of the solenoid valve by feedback from the flow sensor;

[0074] The impedance detection unit collects the voltage or current across the ablation electrode at a rate of 1kHz-10kHz, calculates the dynamic impedance (Z=U / I), and uses a sliding average filter or FFT to eliminate motion artifact interference;

[0075] When the impedance change rate (ΔZ / Δt) exceeds the preset value, the tissue to be treated is judged to be degenerated and frequency tracking is started. By comparing the voltage / current phase difference, if the phase difference is greater than 5°, frequency correction is triggered;

[0076] According to the impedance deviation Adjust frequency, proportional coefficient Determine the speed of response;

[0077] With the goal of minimizing impedance, iteratively approach the target frequency:

[0078] .

[0079] It should be understood that the system consists of a high-frequency generator, a steam generation module, an ablation electrode, an impedance detection unit, and an automatic frequency tracking control module. The core of the system is to monitor changes in resonant impedance in real time (the impedance detection unit) and dynamically adjust the high-frequency output frequency (the automatic frequency tracking control module) to ensure optimal energy transmission efficiency under different tissue conditions (such as dryness and carbonization). It also utilizes high-temperature steam to assist ablation and reduce the risk of tissue carbonization.

[0080] Impedance feedback: The ablation electrode collects the impedance signal of the steam module and transmits it to the control module;

[0081] Frequency calculation: The control module calculates the optimal resonant frequency based on the impedance-frequency characteristic curve (preset or adaptive algorithm);

[0082] Dynamic adjustment: The high-frequency generator switches frequency in real time (typically in the range of 200kHz-1MHz) to match the current tissue load and ensure stable output power.

[0083] Steam synergy: The steam module (heated to 100-150°C) acts synchronously on the target tissue, softening the structure and enhancing high-frequency energy penetration, reducing impedance mutations.

[0084] Initialization stage: preset initial frequency and steam parameters (temperature, flow rate);

[0085] Ablation stage: High-frequency energy and steam are released synchronously, impedance detection is updated every millisecond, and the trigger frequency is fine-tuned;

[0086] Termination condition: Stops after reaching the preset impedance threshold or time. The advantage is that it avoids the "mismatch" problem caused by tissue degeneration in traditional high-frequency ablation, improving surgical efficiency and safety. It is particularly suitable for tissues with fluctuating water content, such as the liver and tumors.

[0087] Technical principles and processes for presetting initial frequency and steam parameters: (1) Presetting initial frequency

[0088] Based on tissue type: The system’s built-in database stores typical impedance-frequency characteristic curves for different tissues (such as liver, muscle, and tumors), and automatically matches the initial frequency based on the surgical site (for example, 300kHz is commonly used for liver, while 500kHz may be selected for adipose tissue).

[0089] Adaptive calibration: After powering on, apply a low-power swept frequency signal (e.g., 200kHz–800kHz) when the electrode contacts the tissue, detect the resonant point (the lowest impedance point), and set this frequency as the initial value.

[0090] (2) Steam parameter preset

[0091] Temperature setting: Based on tissue heat tolerance (e.g., tumor ablation requires 100–120°C, hemostasis requires higher temperatures), the heating module is controlled by a PID algorithm to maintain a steam temperature error of ±2°C.

[0092] Flow rate control: The flow rate (e.g., 5–20 mL / min) is set according to the ablation area (preoperative imaging planning), and the solenoid valve opening is adjusted by feedback from the flow sensor.

[0093] Technical principles and detailed process of trigger frequency fine-tuning: (1) Real-time impedance detection

[0094] Sampling frequency: The impedance detection unit collects the voltage / current across the electrode at a rate of 1kHz–10kHz and calculates the dynamic impedance (Z=U / I).

[0095] Noise processing: Use sliding average filtering or FFT to eliminate motion artifact interference.

[0096] (2) Frequency adjustment trigger conditions

[0097] Threshold trigger: When the impedance change rate (ΔZ / Δt) exceeds a preset value (e.g., 5Ω / ms), tissue degeneration is determined and frequency tracking is initiated.

[0098] Phase detection: By comparing the voltage / current phase difference (reflecting the resonance shift), if the phase difference is greater than 5°, the frequency correction is triggered.

[0099] (3) Fine-tuning the algorithm

[0100] PID closed-loop control: Based on impedance deviation , adjust frequency, proportional coefficient Determines response speed; PID algorithm:

[0101] .

[0102] Gradient descent method: Minimize impedance and iteratively approach the optimal frequency

[0103] .

[0104] How to preset the impedance threshold:

[0105] (1) Based on organizational characteristics

[0106] Experimental data reference: In vitro experiments were performed to determine the impedance values ​​of different tissues at the time of complete ablation (e.g., ablation of liver tissue was considered complete when the impedance increased from an initial 50Ω to 200Ω).

[0107] Clinical experience value: Database integrating expert experience (e.g., tumor ablation threshold is set at 3 times the initial impedance).

[0108] (2) Dynamic threshold calculation

[0109] Normalization: Threshold = × k (the coefficient k is determined by the tissue type, such as k = 2.5–4.0).

[0110] Machine learning prediction: A training model (such as SVM) predicts the ablation endpoint based on the real-time impedance curve and adaptively adjusts the threshold.

[0111] (3) Safety redundancy design

[0112] Double stop judgment: Simultaneously monitor the absolute value of impedance (such as >250Ω) and ablation time (such as 60 seconds). Energy output will be terminated if either condition is met.

[0113] In some specific embodiments, the time monitoring module uses a clock chip as a time reference, and counts the time during the treatment process through a timing circuit to obtain time information, and the time monitoring module transmits the time information to the control module through a line.

[0114] In some specific embodiments, a third adjustment coefficient is set, and the control module reduces the power of the steam generator according to the third adjustment coefficient.

[0115] In some specific embodiments, the leakage protection module is provided with a leakage threshold, and the leakage protection module monitors the leakage of the steam generator circuit in real time through a leakage sensor;

[0116] When the leakage sensor detects that the leakage current reaches the first leakage threshold, a first-level warning is issued. When the leakage sensor detects that the leakage current reaches the second leakage threshold, a second-level warning is issued and power-off measures are taken through the control module.

[0117] It should be understood that lesion tissue type data clearly records the pathological type of the patient's lesion tissue, such as benign tumors, malignant tumors (including cancer types), inflammatory lesions, necrotic tissue, etc. For malignant tumors, this may also include the gene mutation status of cancer cells, receptor expression status (such as hormone receptors, HER2, etc.), and tumor marker levels. This information is crucial for targeted therapy and prognosis assessment.

[0118] Lesion location data accurately describes the specific location of the lesion within the patient's body, including organ name (liver, lung, kidney, etc.), tissue type (such as the left or right lobe of the liver, upper or lower lobe of the lung), and specific location (such as a specific liver segment or lung segment). Combined with image data from medical imaging (such as CT, MRI, and ultrasound), the lesion location is further determined, providing precise positioning information for treatment.

[0119] Lesion severity data categorizes the severity of the disease based on the nature and extent of the diseased tissue. For tumors, this typically includes the stage (e.g., I, II, III, IV), which reflects the tumor's size, depth of invasion, lymph node metastasis, and distant metastasis. For non-neoplastic lesions, a description of severity or activity may also be required. Assessing the extent to which the diseased tissue affects the function of the organ or system in which it resides, such as the degree of liver or kidney function impairment, is crucial for considering the patient's overall health when formulating treatment plans.

[0120] Medical history data details the patient's past medical history, including the name of any previous illness, treatment, and outcome. Pay particular attention to any medical history relevant to the current lesion, such as any previous medical or family history of similar lesions. List any medications the patient has used in the past, including prescription, over-the-counter, and herbal remedies, along with the purpose, dosage, duration, and drug reactions. This is important for avoiding drug interactions and allergic reactions. Record any allergies the patient has to any substance (especially medications and foods), including the allergen, symptoms, and severity. When developing a treatment plan, avoid medications or substances that may cause allergic reactions.

[0121] Clinical symptom data should describe the patient's current symptoms in detail, including their nature (e.g., pain, swelling, cough), location (e.g., chest, abdomen, back), duration (e.g., continuous, intermittent), frequency (e.g., occasional, frequent), and symptom trends (e.g., gradual worsening, gradual improvement). Abnormal findings during physical examination, such as lumps, tenderness, rebound tenderness, and muscle tension, should also be recorded; these signs are important for localizing lesions and assessing disease severity.

[0122] The steam temperature range for pre-set treatment parameters is based on a database of historical treatment cases and current patient data (including lesion type, location, severity, and overall health status). The analysis module uses advanced algorithms to determine a pre-set steam temperature range appropriate for the patient. This range not only maximizes treatment effectiveness but also ensures patient safety and tolerance. For example, for certain temperature-sensitive lesions, the pre-set temperature may be relatively low to protect surrounding normal tissue; whereas for conditions requiring a stronger thermal effect to kill cancer cells, the pre-set temperature may be close to, but not exceeding, the safety limit.

[0123] The steam flow parameter range for pre-set treatment parameters is determined based on the patient's condition and treatment needs. Steam flow directly impacts treatment effectiveness and patient comfort. Excessive flow can cause patient discomfort or increase the risk of complications, while too low a flow may not achieve the desired treatment effect. Therefore, the analysis module comprehensively considers various factors to determine a pre-set steam flow range that ensures treatment effectiveness while minimizing patient discomfort.

[0124] The steam treatment time parameter range within the pre-set treatment parameters is based on the patient's condition and historical case data to determine the desired treatment timeframe. Treatment duration depends on multiple factors, including the size, location, and extent of the lesion, as well as the patient's response to treatment. Precisely pre-set treatment timeframes help therapists better plan treatments, ensuring patients receive treatment within a safe and effective timeframe.

[0125] Treatment parameter steam temperature range: The actual steam temperature parameter, adjusted by the therapist based on the patient's specific needs, is determined based on the preset range. This takes into account individual patient differences (such as temperature sensitivity and skin condition), responses during treatment (such as discomfort or pain), and real-time monitoring data (such as temperature data provided by the temperature monitoring module). Through continuous adjustment and optimization, the actual steam temperature parameter is guaranteed to achieve the optimal treatment effect while ensuring patient safety and comfort.

[0126] The steam flow parameter range for treatment parameters is determined by the preset range and the actual steam flow parameters determined by the patient's actual situation. During actual treatment, the therapist will fine-tune the steam flow based on the patient's response and real-time monitoring data (such as flow data provided by the flow sensor) to ensure treatment effectiveness and patient comfort.

[0127] The steam therapy time parameter range for treatment parameters and the actual steam therapy time parameters used during treatment may be adjusted based on patient response or treatment progress. Therapists will closely monitor changes in the patient's condition and treatment effectiveness, and adjust treatment time accordingly. For example, if the patient experiences discomfort or their condition improves rapidly during treatment, the treatment time may be shortened. Conversely, if the patient's condition is stable and requires longer treatment to consolidate the therapeutic effect, the treatment time may be extended.

[0128] The temperature monitoring module uses high-precision temperature sensors that continuously and accurately measure temperature changes within the steam transmission pipeline. These sensors typically feature fast response, high sensitivity, and excellent stability, ensuring accurate and reliable measurement data. To comprehensively and accurately reflect the temperature distribution within the steam transmission pipeline, temperature sensors are typically placed and sampled at multiple key points along the pipeline. This provides temperature information at various locations, providing rich data support for subsequent data analysis and processing. The analog signals collected by the temperature sensors are converted to digital signals by signal conversion circuits for subsequent processing and analysis. Furthermore, to compensate for signal attenuation and interference during transmission, the signals are amplified. The converted digital signals are transmitted to the control module via dedicated signal transmission lines. These transmission lines typically have excellent insulation and anti-interference capabilities, ensuring stable and accurate signal transmission. Upon receiving the temperature data, the control module immediately displays the current temperature value in real time on the display and simultaneously records the data to an internal storage device. This allows treatment staff to monitor temperature changes within the steam transmission pipeline at any time and query and access historical data as needed.

[0129] The time monitoring module uses a high-precision clock chip as the time reference source. These clock chips offer exceptional stability and accuracy, providing a stable and reliable time signal. They typically utilize advanced semiconductor technology and precision manufacturing processes to ensure accurate and consistent time measurement. To ensure the accuracy and reliability of the time monitoring module, the clock chip regularly synchronizes and calibrates with an external time source (such as a GPS clock or network time server). This eliminates accumulated errors caused by clock drift or external interference, ensuring accurate and consistent time measurement. The time signal provided by the clock chip is typically displayed and measured in seconds. However, depending on the specific application requirements, time units can be converted to minutes, hours, or other larger units for representation and processing.

[0130] The timing circuit is composed of electronic components such as counters and registers. Counters are used to count pulse signals provided by the clock chip, thereby achieving the time measurement function. Registers are used to store counting results and related status information for subsequent processing and analysis. The time monitoring module typically has timing start and stop control functions. Therapists can manually start or stop timing operations according to actual treatment needs, or automatically control the start and stop of timing through a program. This allows for precise control and management of treatment time. To prevent problems such as counter overflow or data loss caused by long-term timing, the time monitoring module is typically equipped with a time overflow detection and alarm processing mechanism. When the counter reaches the preset maximum value, the module automatically issues an alarm signal and stops the timing operation, while also recording the relevant information in a log file for subsequent query and analysis.

[0131] The leakage monitoring module uses specialized leakage sensors to monitor the steam generator's leakage status in real time. These sensors typically operate based on the current transformer principle and can detect even tiny changes in leakage current. When leakage occurs, the sensor immediately generates a corresponding electrical signal change. Leakage sensors are typically installed in key locations within the steam generator, such as near heating elements and electrical connections, areas prone to leakage. Proper installation and layout ensure that the sensors can detect leakage promptly and accurately. Leakage sensors are highly sensitive and have a fast response time. They can detect even weak leakage signals and react quickly, ensuring that leakage issues can be identified and addressed at an early stage. The electrical signal output by a leakage sensor is typically weak and contains noise. Therefore, signal amplification and filtering are required to enhance its strength and purity. An amplifier amplifies the weak signal to a sufficient amplitude for subsequent processing and analysis; a filter removes noise interference from the signal, improving the signal-to-noise ratio. The amplified and filtered signal is then fed into a comparator for threshold determination and comparison. The preset threshold is set based on safety standards and clinical experience. When the signal amplitude exceeds this threshold, it indicates a leakage event. The comparator then outputs a corresponding logic signal indicating whether a leakage event has occurred. To prevent false alarms caused by misjudgments and interference, leakage monitoring modules typically include a delayed confirmation mechanism. After the comparator outputs a leakage signal, the module waits for a period of time (e.g., several seconds) to observe whether the signal persists. If the signal persists for a period exceeding the delay time, a leakage event is confirmed; otherwise, it is considered an interference signal and the alarm is ignored.

[0132] In some specific embodiments, searching a historical treatment case database based on the patient's condition data to find similar treatment information reports includes:

[0133] The analysis module uses the Euclidean distance algorithm to calculate the similarity between the patient's condition data and each case in the historical treatment case database;

[0134] The analysis module is further configured to determine whether each case in the historical treatment case database is a similar treatment information report of the patient based on the relationship between the preset similarity and the actual similarity;

[0135] When the actual similarity is less than the preset similarity, it is determined that the case is not a similar treatment information report of the patient;

[0136] When the actual similarity is greater than or equal to the preset similarity, it is determined that the case is a similar treatment information report of the patient.

[0137] In some specific embodiments, the analysis module calculates the distance between the patient's condition data and each case data in the historical treatment case database using a Euclidean distance algorithm based on the relationship between each case data in the historical treatment case database and the patient's condition data;

[0138] The analysis module assigns a weight to the lesion tissue type data, lesion location data, lesion severity data, medical history information data, and clinical symptom data respectively; the lesion tissue type data, lesion location data, lesion severity data, medical history information data, and clinical symptom data are feature vectors;

[0139] Assume that the lesion tissue type data is X1, the lesion location data is X2, the lesion extent data is X3, the medical history information data is X4, and the clinical symptom data is X5;

[0140] The patient's lesion tissue type data is X1 new , the patient's lesion location data is X2 new , the patient's lesion severity data is X3 new , the patient's medical history information data is X4 new , the patient's clinical symptom data is X5 new ;

[0141] The lesion tissue type data of historical cases in the historical treatment case database is X1 hist , the lesion location data of historical cases in the historical treatment case database is X2 hist The lesion severity data of historical cases in the historical treatment case database is X3 hist , the medical history information data of historical cases in the historical treatment case database is X4 hist , the clinical symptom data of historical cases in the historical treatment case database is X5 hist ;

[0142] The calculation formula of Euclidean distance is:

[0143]

[0144] Among them, a and b are the feature vectors of patients and historical cases respectively, is the weight of the i-th feature, and are the values ​​of vectors a and b on the i-th feature, respectively, and n is the total number of features.

[0145] According to the formula: Calculating distance The similarity score ranges from 0 to 1, and the similarity score is positively correlated with the similarity.

[0146] It should be understood that the new patient's medical data, including lesion tissue type, lesion location, lesion severity, medical history, and clinical symptoms, should be extracted from the electronic medical record (EMR) system. Corresponding case data should be extracted from the historical case database. This data should contain the same feature dimensions as the new patient. The data should be normalized to ensure that the dimensions of different features are consistent. For example, if the value ranges of certain features vary significantly, normalization can be performed to scale the data to the same range (e.g., between 0 and 1). Missing values ​​should be handled. If missing data exists, the mean, median, or interpolation can be used to fill in the missing data. The feature set used to calculate similarity should be determined. For example, lesion tissue type data, lesion location data, lesion severity data, medical history data, and clinical symptom data can be selected as feature vectors. Each feature should be assigned a weight to reflect its importance in the similarity calculation. If certain features are more important than others, they can be given higher weights. The new patient's data and each case in the historical case database should be represented as feature vectors. Each vector contains the numerical representation of the selected features, that is, let the lesion tissue type data be X1, the lesion location data be X2, the lesion extent data be X3, the medical history information data be X4, and the clinical symptom data be X5;

[0147] The patient's lesion tissue type data is X1 new , the patient's lesion location data is X2 new , the patient's lesion severity data is X3 new , the patient's medical history information data is X4 new , the patient's clinical symptom data is X5 new ;

[0148] The lesion tissue type data of historical cases in the historical treatment case database is X1 hist , the lesion location data of historical cases in the historical treatment case database is X2 hist The lesion severity data of historical cases in the historical treatment case database is X3 hist , the medical history information data of historical cases in the historical treatment case database is X4 hist, the clinical symptom data of historical cases in the historical treatment case database is X5 hist .

[0149] For each case in the new patient and the historical case database, we substitute their feature vectors and corresponding weights and calculate the Euclidean distance between them. The smaller the Euclidean distance, the more similar the new patient is to the historical case. Therefore, we can sort the historical cases by distance, selecting the first few with the smallest distances as the most similar cases.

[0150] Euclidean distance comprehensively considers the various characteristic dimensions of new patient data and historical cases. In the medical field, patient information is multifaceted, including physiological indicators (such as blood pressure, blood sugar, and heart rate), medical history (such as previous illnesses and family history), symptoms (such as pain intensity and fever), and examination results (such as imaging studies and laboratory tests). Euclidean distance integrates these different dimensions of information into a single mathematical formula to calculate an overall similarity value, rather than considering each factor in isolation. This provides a more comprehensive and accurate reflection of the differences between new patients and historical cases. For example, when comparing two patients, a single blood pressure value might lead to their being considered similar. However, by considering differences in other dimensions such as blood sugar and medical history, Euclidean distance provides a more objective and comprehensive assessment of similarity. This helps doctors more accurately identify historical cases that are truly similar to the new patient, providing a more reliable basis for developing personalized treatment plans. The Euclidean distance calculates a distance between the new patient data and historical case data in a multidimensional space, which accurately quantifies the degree of similarity between the two. The smaller the distance value, the more similar the new patient is to the historical case; the larger the distance value, the lower the similarity. This precise quantitative method provides doctors with an intuitive and comparable standard, allowing them to sort and filter historical cases based on similarity, quickly locating the case most likely to be suitable for the new patient's treatment plan. For example, when faced with multiple historical cases that may be related to a new patient's condition, doctors can calculate the Euclidean distance and sort these cases from high to low based on their similarity to the new patient, prioritizing cases with high similarity when formulating treatment strategies, thereby improving the efficiency and accuracy of treatment decisions.

[0151] Medical data often contains complex, nonlinear relationships between various factors. Euclidean distance not only accounts for the differences in individual features but also captures the interactions and combined effects between them. By calculating the Euclidean distance between new patient data and historical case data, we can uncover complex relationships hidden within the data, helping doctors discover new disease patterns and treatment patterns. For example, in the treatment of certain diseases, factors such as age, gender, and medical history may interact to significantly influence treatment outcomes. Euclidean distance can help doctors identify potential connections between these factors, allowing them to more comprehensively consider the combined effects of various factors when formulating treatment plans, improving the targeting and effectiveness of treatment. As medical data continues to accumulate and update, Euclidean distance calculations can help doctors discover previously unnoticed similarities. By comparing and analyzing large amounts of historical and new patient data, we can identify patient groups with similar symptoms but different combinations of features, or uncover patterns in the impact of specific factors on disease states. For example, in a study of a specific disease, Euclidean distance calculations revealed that a group of patients with similar symptoms but different causes shared characteristic patterns in gene expression and metabolic levels. This discovery could provide new insights and methods for disease diagnosis and treatment, promoting the in-depth development of medical research. A patient's condition is a dynamic process, and their physiological indicators and symptoms may change as treatment progresses. Euclidean distance can calculate the similarity between new patient data and historical case data in real time, promptly reflecting changes in the patient's condition. Based on similarity calculations at different time points, doctors can dynamically adjust treatment plans to better suit individual patient differences and disease progression. For example, during chemotherapy, a patient's physical condition and the response of tumor cells are constantly changing. By regularly calculating Euclidean distance, doctors can understand the differences between a patient's current condition and historical cases from the early stages of treatment or earlier stages, allowing them to promptly adjust the type, dosage, and frequency of chemotherapy drugs to improve treatment effectiveness and minimize adverse reactions.

[0152] Every patient's response to treatment is unique. Even if their conditions are similar, different patients may react differently to the same treatment plan due to individual differences. Euclidean distance calculation can help doctors monitor individual differences in patients' responses during treatment and provide patients with personalized treatment adjustment recommendations based on changes in similarity with historical cases. For example, after surgical treatment, some patients may recover faster, while others may experience complications or recover more slowly. By comparing the Euclidean distance of these patients' postoperative data with historical cases, doctors can identify factors that cause differences, such as the patient's physical fitness, surgical details, etc., thereby formulating more accurate rehabilitation plans and subsequent treatment plans for each patient.

[0153] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A temperature adaptive regulation system for high-frequency heating steam ablation equipment, characterized in that: include: A high-frequency generator, a steam generating module, an ablation electrode, and an impedance detection unit. The ablation electrode collects the impedance signal of the steam generating module, and the impedance detection unit monitors the voltage or current across the ablation electrode in real time. It also includes: an analysis module, the analysis module is used to provide treatment parameters according to the patient's condition data; A temperature monitoring module, which is used to collect temperature data in the steam transmission pipeline in real time; A time monitoring module, which is used to record time information during the patient's treatment process; a control module, the control module adjusting the power of the steam generator based on the treatment parameter and the temperature data, the control module controlling the operating time of the steam generator based on the treatment parameter and the time information, the control module being configured to receive the impedance signal, and the control module calculating the resonant frequency based on an impedance-frequency characteristic curve; A leakage protection module, which is used to monitor the circuit leakage of the steam generator in real time, and is electrically connected to the control module, analysis module, temperature monitoring module, and time monitoring module; The analysis module obtains treatment information reports of relevant patients through the electronic medical record system (EMR), constructs a historical treatment case database based on the treatment information reports of the relevant patients, searches the historical treatment case database according to the patient's condition data to find similar treatment information reports, and the analysis module provides preset treatment parameters based on the similar treatment information reports, and determines the treatment parameters based on the relationship between the patient's condition data and the similar treatment information reports; When searching the historical treatment case database based on the patient's condition data to find similar treatment information reports, the following steps are included: The analysis module calculates the similarity between the patient's condition data and each case in the historical treatment case database using a Euclidean distance algorithm; The analysis module is further configured to determine whether each case in the historical treatment case database is a similar treatment information report of the patient based on the relationship between the preset similarity and the actual similarity; When the actual similarity is less than the preset similarity, it is determined that the case is not a similar treatment information report of the patient; When the actual similarity is greater than or equal to the preset similarity, it is determined that the case is a similar treatment information report of the patient; The analysis module calculates the distance between the patient's condition data and each case data in the historical treatment case database by using a Euclidean distance algorithm based on the relationship between each case data in the historical treatment case database and the patient's condition data; The analysis module assigns a weight to each of the lesion tissue type data, lesion location data, lesion severity data, medical history information data, and clinical symptom data; the lesion tissue type data, lesion location data, lesion severity data, medical history information data, and clinical symptom data are feature vectors; Assume that the lesion tissue type data is X1, the lesion location data is X2, the lesion extent data is X3, the medical history information data is X4, and the clinical symptom data is X5; The patient's diseased tissue type data is X1 new , the patient's lesion location data is X2 new , the patient's lesion severity data is X3 new , the patient's medical history information data is X4 new , the patient's clinical symptom data is X5 new ; The lesion tissue type data of the historical cases in the historical treatment case database is X1 hist The lesion location data of the historical cases in the historical treatment case database is X2 hist The lesion severity data of the historical cases in the historical treatment case database is X3 hist The medical history information data of the historical cases in the historical treatment case database is X4 hist The clinical symptom data of the historical cases in the historical treatment case database is X5 hist ; The calculation formula of Euclidean distance is: Among them, a and b are the feature vectors of patients and historical cases respectively, is the weight of the i-th feature, and are the values ​​of vectors a and b on the i-th feature, respectively, and n is the total number of features.

2. A temperature adaptive adjustment system for high-frequency heating steam ablation equipment according to claim 1, characterized in that: The treatment information report includes: lesion tissue type data, lesion location data, lesion extent data, medical history information data, and clinical symptom data; The preset treatment parameters and the treatment parameters include: steam temperature parameters, steam flow parameters, and steam treatment time parameters.

3. The temperature adaptive adjustment system for high-frequency heating steam ablation equipment according to claim 2, characterized in that: The temperature monitoring module collects temperature data in the steam transmission pipeline in real time through a temperature sensor, and the temperature monitoring module transmits the real-time collected temperature data in the steam transmission pipeline to the control module through a signal transmission line; Setting a first adjustment coefficient, when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameter, the control module reduces the steam temperature parameter in the treatment parameter according to the first adjustment coefficient; A second adjustment coefficient is set. When the temperature actually monitored by the temperature monitoring module is lower than the steam temperature in the treatment parameter, the control module increases the steam treatment time parameter in the treatment parameter according to the second adjustment coefficient.

4. The temperature adaptive adjustment system for high-frequency heating steam ablation equipment according to claim 3, characterized in that: When the control module detects that the temperature data transmitted in real time by the temperature monitoring module exceeds the steam temperature in the treatment parameter, the control module reduces the power of the steam generator; The high-frequency generator switches the frequency in real time within a frequency range of 200kHz-1MHz, detects the resonance point, and sets the lowest impedance point as the initial value; The temperature monitoring module controls the steam generation module through the PID algorithm based on the heat resistance of the tissue to be treated, maintaining the steam temperature error of ±2°C, setting the flow rate according to the ablation area of ​​the tissue to be treated, and adjusting the opening of the solenoid valve by feedback from the flow sensor; The impedance detection unit collects the voltage or current across the ablation electrode at a rate of 1kHz-10kHz, calculates the dynamic impedance (Z=U / I), and uses a sliding average filter or FFT to eliminate motion artifact interference; When the impedance change rate (ΔZ / Δt) exceeds the preset value, the tissue to be treated is judged to be degenerated and frequency tracking is started. By comparing the voltage / current phase difference, if the phase difference is greater than 5°, frequency correction is triggered; According to the impedance deviation Adjust frequency, proportional coefficient Determine the speed of response; With the goal of minimizing impedance, iteratively approach the target frequency: 。 5. The temperature adaptive adjustment system for high-frequency heating steam ablation equipment according to claim 4, characterized in that: The time monitoring module uses a clock chip as a time reference and counts the time during the treatment process through a timing circuit to obtain the time information. The time monitoring module transmits the time information to the control module through a line.

6. The temperature adaptive adjustment system for high-frequency heating steam ablation equipment according to claim 5, characterized in that: A third adjustment coefficient is set, and the control module reduces the power of the steam generator according to the third adjustment coefficient.

7. The temperature adaptive adjustment system for high-frequency heating steam ablation equipment according to claim 6, characterized in that: The leakage protection module is provided with a leakage threshold, and the leakage protection module monitors the leakage condition of the steam generator circuit in real time through a leakage sensor; When the leakage sensor detects that the leakage current reaches a first leakage threshold, a first-level warning is issued; when the leakage sensor detects that the leakage current reaches a second leakage threshold, a second-level warning is issued and power-off measures are taken through the control module.

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