A radiofrequency therapeutic device

Abstract

The present application provides a radiofrequency therapeutic apparatus, which relates to the field of medical device technology. By collecting dynamic temperature data on the surface of a target tissue, dynamic change information of the temperature field on the surface of the target tissue is obtained in real time; and the temperature control constraints of the radiofrequency therapeutic apparatus are set according to multiple temperature deviation indicators corresponding to the temperature mutation area; the heat distribution law in the temperature mutation area is regionally connected to obtain the temperature compensation boundary corresponding to the temperature mutation area on the surface of the target tissue; the temperature change compensation trend of the radiofrequency therapeutic apparatus is determined according to the temperature compensation boundary and the dynamic temperature data; the multi-level radiofrequency therapeutic signal of the radiofrequency therapeutic apparatus in different temperature fields is determined according to the temperature control constraint and the temperature change compensation trend; the temperature control strategy of the radiofrequency therapeutic apparatus is matched according to the multi-level radiofrequency therapeutic signal, and the temperature control strategy is output. The present application can realize intelligent matching of the temperature control strategy through the multi-level radiofrequency therapeutic signal to improve the stability of the temperature control of the radiofrequency therapeutic apparatus.

Description

Technical Field

[0001] The present application relates to the technical field of medical devices, and more specifically, to a radio frequency therapeutic device. Background Art

[0002] Medical devices refer to a comprehensive system of specialized equipment and control methods used to assist or intervene in the structure or function of the human body through physical, electronic, biological, and mechanical means during disease prevention, diagnosis, treatment, monitoring, and rehabilitation. In modern clinical applications, medical devices are widely used in a variety of fields, including imaging detection, minimally invasive treatment, energy control, and physiological signal monitoring. In particular, in energy-based treatments such as thermotherapy, phototherapy, and radiofrequency, medical device technology achieves simultaneous improvements in treatment accuracy and safety through real-time monitoring and feedback control of tissue status. With the development of artificial intelligence, sensor integration, and precision control technologies, medical devices are gradually evolving towards intelligent and adaptive capabilities.

[0003] However, existing RF therapy device control methods commonly suffer from issues such as delayed temperature acquisition, slow response to sudden changes, a single temperature control strategy, and a lack of spatial compensation mechanisms. This makes it impossible to accurately adjust to the rapid changes in the target tissue surface temperature field during treatment, potentially leading to overheating or insufficient heating of local tissues, thereby reducing the safety and effectiveness of RF therapy. Therefore, how to intelligently match temperature control strategies through multi-level RF therapy signals to improve the stability of RF therapy device temperature control has become an urgent issue. Summary of the Invention

[0004] The present application provides a radio frequency therapeutic apparatus that can realize intelligent matching of temperature control strategies through multi-level radio frequency therapeutic signals to improve the stability of temperature control of the radio frequency therapeutic apparatus.

[0005] The present application provides a radiofrequency therapeutic device, which is controlled by the following control method:

[0006] Place the treatment head of the radiofrequency therapy device on the target tissue surface, collect dynamic temperature data on the target tissue surface, and obtain dynamic change information of the temperature field on the target tissue surface in real time;

[0007] Identifying a temperature mutation area on the target tissue surface through the temperature distribution characteristics in the dynamic change information, and setting temperature control constraints of the radiofrequency therapeutic device according to a plurality of temperature deviation indicators corresponding to the temperature mutation area;

[0008] Performing regional connectivity on the heat distribution pattern in the temperature mutation region to obtain a temperature compensation boundary corresponding to the temperature mutation region on the target tissue surface, and determining a temperature change compensation trend of the radiofrequency therapeutic apparatus based on the temperature compensation boundary and the dynamic temperature data;

[0009] A multi-level radio frequency treatment signal of the radio frequency treatment apparatus in different temperature fields is determined according to the temperature control constraint and the temperature change compensation trend, and a temperature control strategy of the radio frequency treatment apparatus is matched according to the multi-level radio frequency treatment signal.

[0010] In this embodiment, the dynamic change information refers to the continuous distribution and change trend data of the temperature field on the time axis.

[0011] In this embodiment, identifying the temperature mutation area on the target tissue surface by using the temperature distribution characteristics in the dynamic change information specifically includes:

[0012] determining temperature distribution characteristics in the dynamic change information;

[0013] determining a temperature gradient variation interval on the target tissue surface according to the temperature distribution characteristics;

[0014] Determine the dynamic discrimination index of the target tissue surface temperature mutation;

[0015] The temperature mutation area on the surface of the target tissue is identified according to the temperature gradient change interval and the dynamic discrimination index.

[0016] In this embodiment, the temperature mutation area refers to a spatial area where a drastic change occurs in the temperature distribution, exceeds the tissue heat tolerance threshold, and poses a risk to the treatment effect.

[0017] In this embodiment, setting the temperature control constraints of the radiofrequency therapeutic apparatus according to the multiple temperature deviation indicators corresponding to the temperature mutation area specifically includes:

[0018] Determining a temperature fluctuation tolerance value of the radiofrequency therapeutic apparatus according to a plurality of temperature deviation indicators within the temperature mutation area;

[0019] Determine the heat conduction response parameters of the radiofrequency therapy device;

[0020] evaluating a safety level deviation of a target tissue surface based on the temperature fluctuation tolerance value and the heat conduction response parameter;

[0021] The temperature control constraint of the radiofrequency therapy device is determined by the safety level deviation.

[0022] In this embodiment, the heat distribution law in the temperature mutation area is regionally connected to obtain the temperature compensation boundary corresponding to the temperature mutation area on the target tissue surface, specifically including:

[0023] Based on the heat flow direction and gradient distribution characteristics of the heat distribution law, a regional connectivity map of heat distribution is constructed;

[0024] generating multi-level connectivity constraints according to temperature compensation requirements of adjacent mutation regions in the regional connectivity map;

[0025] Thermal balance path planning is performed on the multi-level connectivity constraints, and a temperature compensation boundary is output.

[0026] In this embodiment, the temperature compensation boundary represents the outer boundary of the control area when the radiofrequency therapeutic device controls the temperature of the target tissue surface during radiofrequency treatment.

[0027] In this embodiment, determining the temperature variation compensation trend of the radiofrequency therapeutic apparatus according to the temperature compensation boundary and the dynamic temperature data specifically includes:

[0028] quantifying a spatiotemporal coupling parameter of a temperature change compensation trend according to a time series correlation between the temperature compensation boundary and the dynamic temperature data;

[0029] Combining the heat conduction response parameters of the radiofrequency therapy device with the thermal relaxation characteristics of the tissue, a predictive control variable for temperature change compensation is established;

[0030] Determining the compensation efficiency gradient of the radiofrequency therapeutic apparatus in different temperature compensation strategies according to the spatiotemporal coupling parameter and the predicted control amount;

[0031] The temperature variation compensation trend of the radiofrequency therapeutic apparatus is determined according to the compensation efficiency gradient.

[0032] In this embodiment, determining the multi-level radio frequency treatment signal of the radio frequency treatment apparatus in different temperature fields according to the temperature control constraint and the temperature change compensation trend specifically includes:

[0033] Based on the temperature control constraint and the temperature change compensation trend, constructing temperature mapping quantities of multi-level radio frequency signals in different temperature fields;

[0034] Determine the dynamic evolution characteristics of the temperature field of the radiofrequency therapeutic device in different temperature fields;

[0035] The multi-level radio frequency treatment signals of the radio frequency treatment apparatus in different temperature fields are determined according to the temperature mapping amount and the dynamic evolution characteristics of the temperature field.

[0036] In this embodiment, the temperature control strategy of the radiofrequency therapeutic apparatus is matched according to the multi-level radiofrequency therapeutic signal, and the temperature control strategy with the highest matching degree and the most timely response is output specifically including:

[0037] Phase-align the frequency of the multi-level radiofrequency treatment signal with the energy distribution characteristics of the target temperature field to obtain temperature fitting information of the radiofrequency treatment device;

[0038] Establishing a temperature mapping relationship between the temperature control strategy and the temperature field classification signal;

[0039] The temperature fitting information and the temperature mapping relationship are integrated through a closed-loop feedback verification mechanism to output a temperature control strategy with the highest matching degree and the most timely response.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] The treatment head of the radiofrequency therapy device is placed on the surface of the target tissue, and the dynamic temperature data of the target tissue surface is collected to obtain the dynamic change information of the temperature field on the target tissue surface in real time; the temperature mutation area on the target tissue surface is identified by the temperature distribution characteristics in the dynamic change information, and the temperature control constraint of the radiofrequency therapy device is set according to the multiple temperature deviation indicators corresponding to the temperature mutation area; the heat distribution law in the temperature mutation area is regionally connected to obtain the temperature compensation boundary corresponding to the temperature mutation area on the target tissue surface, and the temperature change compensation trend of the radiofrequency therapy device is determined according to the temperature compensation boundary and the dynamic temperature data; the multi-level radiofrequency therapy signal of the radiofrequency therapy device in different temperature fields is determined according to the temperature control constraint and the temperature change compensation trend, the temperature control strategy of the radiofrequency therapy device is matched according to the multi-level radiofrequency therapy signal, and the temperature control strategy with the highest matching degree and the most timely response is output.

[0042] It can be seen that in this application, the temperature mutation area can be accurately identified based on the dynamic temperature information perception; wherein, by placing the radiofrequency therapy device treatment head on the target tissue surface and collecting the dynamic temperature data of the target tissue surface in real time, the dynamic change information of the tissue surface temperature field can be accurately and quickly obtained, and the temperature change trend and regional hot spots can be finely monitored, effectively solving the problems of temperature monitoring delay and distribution ambiguity in the existing technology; by extracting the dynamic temperature distribution characteristics to identify the temperature mutation area, and setting the temperature control constraints based on multiple temperature deviation indicators, the temperature control mechanism has the active protection capability of the temperature mutation area, thereby avoiding tissue damage caused by abnormal heating and improving radiofrequency therapy. The system improves the temperature control safety and control response efficiency during the treatment process; by analyzing the heat distribution law in the temperature mutation area, constructing the heat distribution connectivity map, and generating a multi-level temperature compensation boundary, combining the dynamic temperature data to establish the temperature change compensation trend, so that the temperature compensation process has spatial coherence and temporal predictability, effectively improving the temperature stability during the treatment process, and overcoming the local lag and global imbalance of the existing compensation strategy; through the frequency phase alignment and closed-loop feedback mechanism, the dynamic matching between the multi-level RF signal and the tissue thermal response is achieved, so as to intelligently output the control strategy according to the temperature evolution characteristics of the actual treatment area, improve the individual adaptability of RF treatment and the intelligent level of temperature control, and overcome the limitations of the traditional fixed control mode and slow adjustment.

[0043] In summary, the technical solution adopted in this application can realize intelligent matching of temperature control strategies through multi-level radio frequency treatment signals to improve the stability of temperature control of radio frequency treatment devices. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0045] Figure 1 is an exemplary flow chart of a control method of a radiofrequency therapeutic apparatus provided in the present application;

[0046] Figure 2 is a schematic diagram of a process for determining temperature control constraints provided in this application;

[0047] Figure 3 This is a flow chart of determining the temperature change compensation trend provided by this application. DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying 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 of 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.

[0049] The embodiment of the present application provides a radiofrequency therapeutic device, the core of which is to place the treatment head of the radiofrequency therapeutic device on the surface of the target tissue, collect dynamic temperature data of the target tissue surface, and obtain dynamic change information of the temperature field on the target tissue surface in real time; identify the temperature mutation area on the target tissue surface through the temperature distribution characteristics in the dynamic change information, and set the temperature control constraint of the radiofrequency therapeutic device according to the multiple temperature deviation indicators corresponding to the temperature mutation area; regionally connect the heat distribution law in the temperature mutation area to obtain the temperature compensation boundary corresponding to the temperature mutation area on the target tissue surface, and determine the temperature change compensation trend of the radiofrequency therapeutic device according to the temperature compensation boundary and the dynamic temperature data; determine the multi-level radiofrequency treatment signal of the radiofrequency therapeutic device in different temperature fields according to the temperature control constraint and the temperature change compensation trend, match the temperature control strategy of the radiofrequency therapeutic device according to the multi-level radiofrequency treatment signal, and output the temperature control strategy with the highest matching degree and the most timely response. The above scheme can realize intelligent matching of temperature control strategy through multi-level radiofrequency treatment signal to improve the stability of temperature control of radiofrequency therapeutic device.

[0050] In order to better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG, this figure is an exemplary flow chart of a control method of a radiofrequency therapeutic apparatus according to this embodiment of the present application, and the control method includes the following steps:

[0051] In step S1, the treatment head of the radiofrequency therapy device is placed on the target tissue surface, the dynamic temperature data of the target tissue surface is collected, and the dynamic change information of the temperature field on the target tissue surface is obtained in real time.

[0052] In specific implementation, the treatment head of the radiofrequency therapy device is placed on the surface of the target tissue, and the dynamic temperature data of the target tissue surface is collected in the following manner, namely: first, the treatment head of the radiofrequency therapy device is directly attached to the surface of the target tissue, and an infrared thermal imager or a distributed micro-thermocouple array is deployed synchronously during the treatment process. The infrared thermal imager is used for non-contact, wide-area rapid temperature scanning, covering the entire treatment area; the thermocouple array is fixed to multiple key points on the tissue surface by microneedles or patches, providing high-precision, local temperature data. The collected data is transmitted to the main control processing system via a serial port or wirelessly, and the storage unit of the main control processing system is read at fixed time intervals, wherein the time interval can be set according to the actual situation, and there is no limitation here. For example, every 100 milliseconds, the temperature information read is used as the dynamic temperature data of the target tissue surface.

[0053] It should be noted that, in the present application, dynamic temperature data refers to a set of temperature values ​​of multiple points on the surface of the target tissue that are collected during radiofrequency treatment and change with time.

[0054] Furthermore, in practice, real-time acquisition of dynamic changes in the target tissue surface temperature field can be achieved through the following method: a high-temporal-resolution temperature sensing and data synchronization system can be deployed by synergizing an infrared thermal imaging module with a micro-thermocouple array. The infrared thermal imager is used to acquire a wide-area, continuous temperature distribution map, with a high frame rate ≥10 fps and a high resolution ≥0.1°C. The micro-thermocouple array is mounted on the surface of the treatment area, either attached or embedded, to accurately capture temperatures at multiple key points, compensating for errors in the thermal image caused by angle or occlusion. All sensor data is synchronously transmitted via a high-speed interface to an embedded processing unit. This processing unit integrates image reconstruction and interpolation algorithms to fuse point and surface temperature data, constructing a two-dimensional temperature distribution map in real time and generating a three-dimensional temperature field along the time axis. To meet real-time requirements, the system must have a data processing closed-loop time of ≤200 ms to continuously and accurately acquire dynamic changes in the target tissue surface temperature field.

[0055] It should be noted that, in this application, the target tissue surface temperature field represents the two-dimensional spatial temperature distribution matrix of the target tissue surface temperature in a time series; dynamic change information refers to the continuous distribution and change trend data of the temperature field on the time axis.

[0056] In step S2, the temperature mutation area on the target tissue surface is identified by the temperature distribution characteristics in the dynamic change information, and the temperature control constraints of the radiofrequency therapeutic device are set according to multiple temperature deviation indicators corresponding to the temperature mutation area.

[0057] In this embodiment, identifying the temperature mutation area on the target tissue surface by using the temperature distribution characteristics in the dynamic change information can be achieved by using the following steps:

[0058] determining temperature distribution characteristics in the dynamic change information;

[0059] determining a temperature gradient variation interval on the target tissue surface according to the temperature distribution characteristics;

[0060] Determine the dynamic discrimination index of the target tissue surface temperature mutation;

[0061] The temperature mutation area on the surface of the target tissue is identified according to the temperature gradient change interval and the dynamic discrimination index.

[0062] In specific implementation, a two-dimensional temperature distribution map for each frame is first extracted from the dynamic change information, and spatiotemporal analysis of the continuous images is performed. Three types of features are used: spatial distribution features, which calculate the spatial gradient, edge information, and hotspot clusters within each temperature image frame; temporal change features, which record the temperature change rate at each spatial point; and statistical features, including the maximum temperature value, mean, standard deviation, and skewness coefficient for each frame. These three types of features are combined through sliding window analysis and differential processing to form a set of feature description vectors for determining regional thermal stability. These feature description vectors are then used as the temperature distribution features within the dynamic change information. Next, a spatial gradient map is calculated for each temperature image frame, and a gradient operator, such as the Sobel operator, is used to calculate the temperature change rate pixel by pixel to obtain a temperature gradient image. The temperature gradient in the temperature gradient image is then statistically segmented by gradient value to form multiple temperature gradient variation ranges, such as low gradient 0–1°C / cm, medium gradient 1–3°C / cm, and high gradient >3°C / cm. (These are not limited here.) Next, indicators for identifying sudden changes are extracted from the dynamic temperature field: the instantaneous temperature rise rate threshold, for example, a temperature change rate > 0.5°C / s; the local gradient change, for example, a ∇T change > a set threshold; the temperature jump amplitude, for example, a temperature difference between adjacent pixels greater than 3°C; and a high-frequency perturbation fluctuation indicator. High-frequency thermal perturbation energy can be extracted through Fourier analysis (not specifically defined here) and the resulting indicators are used as dynamic discrimination indicators. Finally, the temperature gradient of the current frame's temperature image is combined with historical temperature change trends to mark regions with significant gradient changes that meet multiple dynamic discrimination indicators. A region growing algorithm can be used to expand adjacent high-risk pixels to form a continuous temperature sudden change region, represented by a closed boundary.

[0063] It should be noted that, in this application, temperature distribution characteristics refer to the characteristic set of temperature change trends in space and time in the treatment area; temperature gradient change interval refers to the interval divided by the temperature change amplitude per unit distance, which is used to quantify the intensity of local heat diffusion; dynamic discrimination index refers to a multidimensional parameter set that quantitatively evaluates the degree of temperature mutation in time and space; temperature mutation area refers to the spatial area where drastic changes occur in temperature distribution, exceed the tissue heat tolerance threshold, and pose a risk to the treatment effect.

[0064] Preferably, in this embodiment, the temperature control constraints of the radiofrequency therapeutic apparatus are set according to the multiple temperature deviation indicators corresponding to the temperature mutation area, referring to Figure 2 As shown in FIG, this figure is a schematic diagram of a process for determining temperature control constraints in some embodiments of the present application. In this embodiment, determining the temperature control constraints can be implemented using the following steps:

[0065] In step S21, a temperature fluctuation tolerance value of the radiofrequency therapeutic apparatus is determined according to a plurality of temperature deviation indicators within the temperature mutation area;

[0066] In step S22, the heat conduction response parameter of the radiofrequency therapeutic apparatus is determined;

[0067] In step S23, the safety level deviation of the target tissue surface is evaluated based on the temperature fluctuation tolerance value and the heat conduction response parameter;

[0068] In step S24, the temperature control constraint of the radiofrequency therapeutic apparatus is determined according to the safety level deviation.

[0069] In specific implementation, the following temperature deviation indicators are first extracted from the identified temperature mutation regions: maximum temperature difference within the region, local temperature fluctuation rate, spatial temperature standard deviation, and temporal fluctuation frequency. Based on these temperature deviation indicators, combined with tissue type and clinical safety limits, a temperature fluctuation tolerance is set: the maximum allowable temperature difference range and upper limit for the heating rate within a given temporal and spatial scale. For example, for skin tissue, the tolerance fluctuation is ±1.5°C, and the heating rate is <0.3°C / s. Next, a tissue heat conduction simulation model is used, combined with actual device parameters including power density, treatment head contact area, frequency, and tissue thermal parameters including thermal conductivity, specific heat capacity, and blood perfusion rate, to establish a dynamic relationship between RF heating and temperature response. The response curve of the treatment device is then derived through experiments or numerical simulations. The thermal response delay time, thermal diffusion radius, and thermal inertia coefficient are extracted from the response curve as the thermal conduction response parameters of the RF treatment device. The actual temperature data is then compared with the fluctuation tolerance to calculate the temperature difference exceeding the limit and the temperature rise exceeding the limit for each region. At the same time, combined with the thermal response parameters, the prediction error window caused by control hysteresis is calculated. For example, the regional temperature may rise by 2°C after 1 second. The safety level deviation scoring model is established based on these two dimensions, namely: ,in, Indicates that the security level deviates from the scoring model; Indicates the portion where the actual temperature difference or rate exceeds the tolerance threshold; Indicates the error prediction range caused by thermal inertia or hysteresis; and The safety level deviation scoring model outputs the target tissue surface's safety level deviation, representing the tissue sensitivity weight. Finally, the temperature control strategy is dynamically adjusted based on the safety level corresponding to the deviation, and this adjustment process serves as the temperature control constraint for the RF therapy device. The temperature control constraints for this RF therapy device include: safety level → normal output (original planned energy); warning level → power output reduced by 10% and cooling intervals increased; risk level → activation of a dynamic closed-loop temperature control mechanism (temperature control instructions updated once per second); and high-risk level → forced termination of treatment and issuance of an alarm.

[0070] It should be noted that, in this application, the temperature deviation index refers to the characteristic data of internal heterogeneity and thermal instability of the temperature field; the temperature fluctuation tolerance value refers to the acceptable temperature change range of the therapeutic device within a unit time and area; the heat conduction response parameter refers to the parameter set that describes the dynamic relationship between the energy output of the therapeutic device and the temperature response of the tissue; the safety level deviation refers to the comprehensive deviation between the current thermal state of the tissue and the safety control limit; the temperature control constraint refers to the energy output parameter range dynamically limited according to the current thermal state.

[0071] In step S3, the heat distribution law in the temperature mutation area is regionally connected to obtain the temperature compensation boundary corresponding to the temperature mutation area on the target tissue surface, and the temperature change compensation trend of the radiofrequency therapeutic device is determined based on the temperature compensation boundary and the dynamic temperature data.

[0072] In this embodiment, the heat distribution pattern in the temperature mutation region is regionally connected to obtain the temperature compensation boundary corresponding to the temperature mutation region on the target tissue surface. The following steps can be used:

[0073] Based on the heat flow direction and gradient distribution characteristics of the heat distribution law, a regional connectivity map of heat distribution is constructed;

[0074] generating multi-level connectivity constraints according to temperature compensation requirements of adjacent mutation regions in the regional connectivity map;

[0075] Thermal balance path planning is performed on the multi-level connectivity constraints, and a temperature compensation boundary is output.

[0076] In the specific implementation, first, the gradient information in the continuous temperature field image is used to calculate the heat flow vector field, and the Fourier method or the temperature gradient vector difference method is used to obtain the main heat conduction direction of each point, where the main heat conduction direction is negatively correlated with the temperature gradient and points to the heat diffusion path; then the first-order and second-order gradient analysis is performed on the boundary of the temperature mutation area to obtain the temperature change rate and curvature change trend, reflecting the turning, diffusion or blocking characteristics of the heat flow in space; and the mutation area is discretized into graph structure units, where the nodes of the graph structure units are high heat flux density sub-areas, the edges are the main heat flow connection paths, and the heat flux intensity, direction consistency and spatial distance are used as edge weights to construct a regional connectivity map of heat distribution. Then, for each mutation node in the regional connectivity graph, a temperature deviation is calculated. This deviation is the difference from the critical physiological temperature or target therapeutic temperature. The required thermal compensation power or cooling capacity is then assessed based on the thermal conductivity and heat transfer capacity of the surrounding environment. Adjacent nodes are then classified into multi-level connectivity relationships according to the following rules: strong connectivity (level 1), where the heat flow direction is clear and the temperature difference is large, requiring timely compensation; weak connectivity (level 2), where the heat flow is unstable or intermittent, requiring auxiliary control; and relay connectivity (level 3), where transition regions in the heat transfer channel can serve as buffer heat paths. These classified three-level connectivity relationships are encoded into a table of thermal compensation control rules, resulting in multi-level connectivity constraints. Finally, a set of thermal balance equations is constructed based on the temperature distribution, heat flow direction, heat capacity, and heat transfer efficiency of each region in the regional connectivity graph. The thermal balance equations are determined by subtracting the sum of the heat input and output between a region and its neighboring regions from the sum of the net heat required to achieve the current target temperature. Then, the shortest path optimization algorithm is used to find the optimal thermal compensation path from the high heat source to the low heat area in the regional connectivity graph, giving priority to the first-level connectivity area, and then progressively adjusting the second-level and third-level connectivity areas to ensure the overall thermal field balance. The path convergence points and boundary diffusion points are extracted from the final thermal balance path network, and the temperature compensation boundary is extracted through boundary interpolation and morphological contours.

[0077] It should be noted that, in this application, the direction of heat flow refers to the dominant path direction of heat conduction from the high-temperature zone to the low-temperature zone in the tissue; the gradient distribution characteristics refer to the rate and direction of temperature change in space; the regional connectivity map represents the connectivity relationship of heat transfer between temperature mutation zones; the temperature compensation requirement represents the additional heat input or cooling required to maintain the target temperature in a specific area; the multi-level connectivity constraint refers to the hierarchical control relationship between different temperature mutation zones based on thermal demand and thermal connection strength; the temperature compensation boundary represents the outer boundary of the control area when the radiofrequency therapy device controls the temperature of the target tissue surface during radiofrequency treatment.

[0078] Preferably, in this embodiment, the temperature compensation trend of the radiofrequency therapeutic apparatus is determined according to the temperature compensation boundary and the dynamic temperature data, referring to Figure 3As shown in FIG, this figure is a schematic diagram of a process for determining a temperature change compensation trend in some embodiments of the present application. In this embodiment, determining the temperature change compensation trend can be achieved by using the following steps:

[0079] In step S31, according to the time series correlation between the temperature compensation boundary and the dynamic temperature data, the spatiotemporal coupling parameters of the temperature change compensation trend are quantified;

[0080] In step S32, the predicted control quantity of temperature change compensation is established by combining the heat conduction response parameters of the radiofrequency therapeutic device and the thermal relaxation characteristics of the tissue;

[0081] In step S33, the compensation efficiency gradient of the radiofrequency therapeutic apparatus in different temperature compensation strategies is determined according to the spatiotemporal coupling parameter and the predicted control amount;

[0082] In step S34, the temperature variation compensation trend of the radiofrequency therapeutic apparatus is determined according to the compensation efficiency gradient.

[0083] In specific implementation, dynamic temperature data is first arranged in a time series, and temperature changes within a time period are analyzed using a sliding window method. Time series correlation analysis is used to evaluate the relationship between temperature changes and the temperature compensation boundary at each time point, thereby generating a time series of dynamic temperature data. Then, by analyzing the temporal variation pattern of the temperature compensation boundary and its corresponding temperature variation, a spatiotemporal coupling model is constructed to quantify the spatiotemporal distribution characteristics of compensation demand and temperature variation. Common methods include wavelet transforms and spatiotemporal autoregressive models. The spatiotemporal coupling model outputs quantified spatiotemporal coupling parameters that represent the temperature variation compensation trend. Next, the thermal conductivity parameters of the radiofrequency therapy device, including the thermal response delay time and thermal diffusion radius, are combined with the thermal relaxation characteristics of the tissue, including specific heat capacity, thermal conductivity, and blood flow, to construct a tissue temperature variation model. This temperature variation model describes how tissue accumulates and dissipates heat over time and space under radiofrequency energy input. Using these thermal conductivity parameters, numerical simulations are used to predict the temperature response of the tissue under different treatment conditions. Based on the simulation results, the temperature variation compensation at different stages is calculated, resulting in a predictive control variable for temperature variation. Then, the compensation effectiveness of the RF therapy device at different treatment stages was evaluated by combining spatiotemporal coupling parameters and predictive control variables. By comparing the instantaneous and cumulative effects of temperature changes, the compensation effectiveness gradient (i.e., the variation in effectiveness of the temperature compensation strategy at different temporal and spatial locations) was calculated. This calculation method included gradient descent or Lagrange multiplier methods. The temperature field gradient was used to measure the sensitivity of different regions to temperature changes. The compensation strategy was optimized in conjunction with the control variable. In high-efficiency regions, the RF output power was increased; in low-efficiency regions, the power was reduced or cooling was enhanced. This resulted in the compensation effectiveness gradient of the RF therapy device under different temperature compensation strategies. Finally, based on the compensation effectiveness gradient, the RF therapy device's response to temperature compensation at different stages was analyzed. Temperature change data was smoothed using exponential smoothing or weighted averaging to extract the compensation trend of temperature changes during treatment. The effectiveness gradients of each region were combined with the RF therapy device's control strategy to determine the overall temperature compensation trend of the RF therapy device. For example, when the temperature compensation effectiveness reached a predetermined threshold, the energy output was adjusted; when the compensation effectiveness was low, additional cooling was applied or the treatment intensity was reduced.

[0084] It should be noted that, in the present application, the spatiotemporal coupling parameters of the temperature change compensation trend represent the mutual dependence relationship between temperature changes in time and space; the predicted control quantity represents the control input quantity required to predict future temperature changes based on the heat conduction parameters and thermal relaxation characteristics; the compensation efficiency gradient refers to the description of the rate of change of the temperature compensation effect in time and space under different temperature control strategies; the heat conduction response parameter represents the technical parameter describing the relationship between the energy output of the radiofrequency therapy device and the temperature response of the tissue; the tissue thermal relaxation characteristics represent the reaction speed and dissipation characteristics of the tissue to heat; the temperature change compensation trend refers to the temperature control adjustment path determined according to the change of the compensation efficiency gradient during the treatment process.

[0085] In step S4, the multi-level radio frequency treatment signal of the radio frequency treatment device in different temperature fields is determined based on the temperature control constraint and the temperature change compensation trend, and the temperature control strategy of the radio frequency treatment device is matched according to the multi-level radio frequency treatment signal, and the temperature control strategy with the highest matching degree and the most timely response is output.

[0086] In this embodiment, determining the multi-level radio frequency treatment signal of the radio frequency treatment device in different temperature fields according to the temperature control constraint and the temperature change compensation trend can be achieved by the following steps:

[0087] Based on the temperature control constraint and the temperature change compensation trend, constructing temperature mapping quantities of multi-level radio frequency signals in different temperature fields;

[0088] Determine the dynamic evolution characteristics of the temperature field of the radiofrequency therapeutic device in different temperature fields;

[0089] The multi-level radio frequency treatment signals of the radio frequency treatment apparatus in different temperature fields are determined according to the temperature mapping amount and the dynamic evolution characteristics of the temperature field.

[0090] In specific implementation, first, the temperature fluctuation tolerance value in the temperature control constraint, the heat conduction response parameter and the temperature compensation efficiency gradient in the temperature change compensation trend are combined to determine the adjustment range of the temperature control strategy in each temperature field, and the weighted average method or normalization method is used to quantify the temperature requirements of different areas and generate temperature mapping quantities; according to the temperature compensation requirements of different areas, a multi-level temperature mapping function is constructed, and each temperature interval, including: low temperature zone, medium temperature zone, and high temperature zone, is mapped to a different RF output intensity. The mapping process can simulate the heat conduction process through the finite element analysis method to generate temperature change curves corresponding to different RF signals, thereby completing the construction of temperature mapping quantities of multi-level RF signals in different temperature fields. Then, based on the temperature field data, combined with the heat conduction model and dynamic temperature data, the time evolution characteristics of the temperature field during radiofrequency treatment are analyzed. Finite difference method or finite element method can be used for numerical simulation to simulate the temperature change trends in different regions; the change characteristics of the temperature field at different time points are extracted, and attention is paid to the evolution process of the temperature mutation zone, thermal equilibrium zone and temperature stability zone. Through multiple simulations and comprehensive historical data, the dynamic evolution patterns under different temperature fields are extracted, and the future temperature change trend is predicted. The predicted temperature change trend is used as the dynamic evolution characteristic of the temperature field of the radiofrequency therapy device in different temperature fields. Finally, the RF treatment signal is mapped to the temperature mapping amount, and RF power levels are set for different temperature intervals, such as low power, medium power, and high power, and their corresponding temperature ranges. The signal output strength of different temperature areas is adjusted according to the dynamic evolution characteristics of the temperature field. Based on the spatiotemporal evolution characteristics of the temperature field and the temperature mapping amount, multiple RF signal levels are set, including level 1, level 2, and level 3 signals, and optimized according to the temperature requirements corresponding to each level. Adaptive filters or feedback adjustment algorithms can be used to dynamically adjust the RF signal. During the treatment process, the RF signal output is adjusted in real time based on the real-time monitored temperature data and the evolution trend of the temperature field. For example, if the temperature in a certain area exceeds the safe range, the RF signal strength is reduced, and vice versa, the RF output is increased. The output result is used as the multi-level RF treatment signal of the RF therapy device in different temperature fields.

[0091] It should be noted that in this application, the temperature mapping quantity is a quantitative function that describes the intensity of the radio frequency treatment signal corresponding to different temperature ranges; the dynamic evolution characteristics of the temperature field describe the law of change of the temperature field over time, including characteristics such as temperature change rate, heat flow distribution, and temperature stability; multi-level radio frequency treatment signal refers to the design and output of radio frequency signals at multiple power levels according to the requirements of different temperature fields.

[0092] In this embodiment, matching the temperature control strategy of the radio frequency therapeutic apparatus according to the multi-level radio frequency therapeutic signal can be achieved by the following steps:

[0093] Phase-align the frequency of the multi-level radiofrequency treatment signal with the energy distribution characteristics of the target temperature field to obtain temperature fitting information of the radiofrequency treatment device;

[0094] Establishing a temperature mapping relationship between the temperature control strategy and the temperature field classification signal;

[0095] The temperature fitting information and the temperature mapping relationship are integrated through a closed-loop feedback verification mechanism to output a temperature control strategy with the highest matching degree and the most timely response.

[0096] In specific implementation, the frequency, duty cycle, and energy transfer rate of each level of the multi-level RF treatment signal are first extracted from the multi-level RF treatment signal. Then, based on dynamic temperature data, thermal imaging technology combined with numerical modeling is used to analyze the energy concentration areas, heat conduction paths, and heat dissipation areas in the temperature field. The signal is then subjected to time-frequency analysis using a short-time Fourier transform. The energy peak of the treatment signal is frequency-domain phase-matched with the heat energy concentration area in the temperature field. A phase synchronization adjustment algorithm is used to achieve temporal coherence between the signal and the thermal response of the target area. The matching result is output in the form of a "signal frequency-temperature response" mapping, which is used as the temperature fitting information of the RF treatment device. Then, key indicators are extracted from the existing temperature control parameters, including maximum output power, heating rate, and safe temperature threshold. The temperature field is divided into multiple temperature levels: a low-temperature stabilization zone, a medium-temperature treatment zone, and a high-temperature warning zone. The corresponding RF signal levels are associated with the temperature control parameters. A one-to-one mapping relationship is established between different temperature control strategies and corresponding RF signal levels using a logical decision tree or multidimensional interpolation algorithm. Finally, a real-time temperature monitoring module is introduced, such as an infrared thermal imaging module or an embedded thermocouple array, to collect dynamic temperature data during the treatment process as a feedback signal. Based on the error between the feedback temperature and the predicted temperature, a fuzzy adaptive control algorithm or a proportional integral differential (PID) algorithm is used to correct the strategy. The temperature fitting information (the relationship between signal frequency and thermal response) and the temperature mapping relationship (the relationship between strategy and signal matching) are input into the control algorithm. After fusion, the optimal strategy parameter combination is output, including radio frequency power, frequency modulation period, action time, etc., forming a real-time update mechanism, continuously optimizing parameters within the closed-loop system, and outputting the temperature control strategy with the highest matching degree and the most timely response.

[0097] It should be noted that, in this application, the energy distribution characteristics of the target temperature field refer to the spatial distribution pattern of thermal energy density generated by radio frequency heating in different areas of the tissue surface and its interior; temperature fitting information refers to the data structure of the synchronization characteristics between the radio frequency signal frequency and the tissue temperature response; the temperature field grading signal refers to the radio frequency control signal level corresponding to different temperature intervals; the temperature mapping relationship refers to the correspondence rules between the temperature control strategy parameters and the radio frequency signal levels required for different temperature intervals.

[0098] It can be seen that in this application, the temperature mutation area can be accurately identified based on the dynamic temperature information perception; wherein, by placing the radiofrequency therapy device treatment head on the target tissue surface and collecting the dynamic temperature data of the target tissue surface in real time, the dynamic change information of the tissue surface temperature field can be accurately and quickly obtained, and the temperature change trend and regional hot spots can be finely monitored, effectively solving the problems of temperature monitoring delay and distribution ambiguity in the existing technology; by extracting the dynamic temperature distribution characteristics to identify the temperature mutation area, and setting the temperature control constraints based on multiple temperature deviation indicators, the temperature control mechanism has the active protection capability of the temperature mutation area, thereby avoiding tissue damage caused by abnormal heating and improving radiofrequency therapy. Temperature control safety and control response efficiency during treatment; by analyzing the heat distribution law in the temperature mutation area, constructing a heat distribution connectivity map, and generating a multi-level temperature compensation boundary, combining dynamic temperature data to establish a temperature change compensation trend, so that the temperature compensation process has spatial coherence and time predictability, effectively improving the temperature stability during treatment, and overcoming the local lag and global imbalance of existing compensation strategies; through frequency phase alignment and closed-loop feedback mechanism, dynamic matching between multi-level RF signals and tissue thermal responses is achieved, so as to intelligently output control strategies based on the temperature evolution characteristics of the actual treatment area, improve the individual adaptability of RF treatment and the intelligent level of temperature control, and overcome the limitations of traditional control modes that are fixed and slow to adjust. In summary, the technical solution adopted in this application can achieve intelligent matching of temperature control strategies through multi-level RF treatment signals to improve the stability of temperature control of RF therapeutic devices.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0101] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A radiofrequency therapeutic apparatus, characterized in that: The radiofrequency therapeutic apparatus is controlled by the following control method: Place the treatment head of the radiofrequency therapy device on the target tissue surface, collect dynamic temperature data on the target tissue surface, and obtain dynamic change information of the temperature field on the target tissue surface in real time; Identifying a temperature mutation area on the target tissue surface through the temperature distribution characteristics in the dynamic change information, and setting temperature control constraints of the radiofrequency therapeutic device according to a plurality of temperature deviation indicators corresponding to the temperature mutation area; Performing regional connectivity on the heat distribution pattern in the temperature mutation region to obtain a temperature compensation boundary corresponding to the temperature mutation region on the target tissue surface, and determining a temperature change compensation trend of the radiofrequency therapeutic apparatus based on the temperature compensation boundary and the dynamic temperature data; Based on the temperature control constraints and the temperature change compensation trend, the multi-level radio frequency treatment signal of the radio frequency treatment device in different temperature fields is determined, and the temperature control strategy of the radio frequency treatment device is matched according to the multi-level radio frequency treatment signal, and the temperature control strategy with the highest matching degree and the most timely response is output.

2. A radiofrequency therapeutic apparatus as claimed in claim 1, characterized in that: The dynamic change information refers to the continuous distribution and change trend data of the temperature field on the time axis.

3. A radiofrequency therapeutic apparatus as claimed in claim 1, characterized in that: Identifying the temperature mutation area on the target tissue surface by using the temperature distribution characteristics in the dynamic change information specifically includes: determining temperature distribution characteristics in the dynamic change information; determining a temperature gradient variation interval on the target tissue surface according to the temperature distribution characteristics; Determine the dynamic discrimination index of the target tissue surface temperature mutation; The temperature mutation area on the surface of the target tissue is identified according to the temperature gradient change interval and the dynamic discrimination index.

4. A radiofrequency therapeutic apparatus as claimed in claim 1, characterized in that: The temperature mutation area refers to a spatial area where the temperature distribution changes drastically, exceeds the tissue heat tolerance threshold, and poses a risk to the treatment effect.

5. The radiofrequency therapeutic apparatus according to claim 1, wherein: Setting the temperature control constraints of the radiofrequency therapeutic apparatus according to the multiple temperature deviation indicators corresponding to the temperature mutation area specifically includes: Determining a temperature fluctuation tolerance value of the radiofrequency therapeutic apparatus according to a plurality of temperature deviation indicators within the temperature mutation area; Determine the heat conduction response parameters of the radiofrequency therapy device; evaluating a safety level deviation of a target tissue surface based on the temperature fluctuation tolerance value and the heat conduction response parameter; The temperature control constraint of the radiofrequency therapy device is determined by the safety level deviation.

6. The radiofrequency therapeutic apparatus according to claim 1, wherein: Performing regional connectivity on the heat distribution law in the temperature mutation region to obtain the temperature compensation boundary corresponding to the temperature mutation region on the surface of the target tissue specifically includes: Based on the heat flow direction and gradient distribution characteristics of the heat distribution law, a regional connectivity map of heat distribution is constructed; generating multi-level connectivity constraints according to temperature compensation requirements of adjacent mutation regions in the regional connectivity map; Thermal balance path planning is performed on the multi-level connectivity constraints, and a temperature compensation boundary is output.

7. The radiofrequency therapeutic apparatus according to claim 1, wherein: The temperature compensation boundary represents the outer boundary of the control area when the radiofrequency therapeutic device controls the temperature of the target tissue surface during radiofrequency treatment.

8. The radiofrequency therapeutic apparatus according to claim 1, wherein: Determining the temperature variation compensation trend of the radiofrequency therapeutic apparatus according to the temperature compensation boundary and the dynamic temperature data specifically includes: quantifying a spatiotemporal coupling parameter of a temperature change compensation trend according to a time series correlation between the temperature compensation boundary and the dynamic temperature data; Combining the heat conduction response parameters of the radiofrequency therapy device with the thermal relaxation characteristics of the tissue, a predictive control variable for temperature change compensation is established; Determining the compensation efficiency gradient of the radiofrequency therapeutic apparatus in different temperature compensation strategies according to the spatiotemporal coupling parameter and the predicted control amount; The temperature variation compensation trend of the radiofrequency therapeutic apparatus is determined according to the compensation efficiency gradient.

9. The radiofrequency therapeutic apparatus according to claim 1, wherein: Determining the multi-level radio frequency treatment signal of the radio frequency treatment device in different temperature fields according to the temperature control constraint and the temperature change compensation trend specifically includes: Based on the temperature control constraint and the temperature change compensation trend, constructing temperature mapping quantities of multi-level radio frequency signals in different temperature fields; Determine the dynamic evolution characteristics of the temperature field of the radiofrequency therapeutic device in different temperature fields; The multi-level radio frequency treatment signals of the radio frequency treatment apparatus in different temperature fields are determined according to the temperature mapping amount and the dynamic evolution characteristics of the temperature field.

10. The radiofrequency therapeutic apparatus according to claim 1, wherein: The temperature control strategy of the radiofrequency therapeutic device is matched according to the multi-level radiofrequency treatment signal. The temperature control strategy with the highest output matching degree and the most timely response includes: Phase-align the frequency of the multi-level radiofrequency treatment signal with the energy distribution characteristics of the target temperature field to obtain temperature fitting information of the radiofrequency treatment device; Establishing a temperature mapping relationship between the temperature control strategy and the temperature field classification signal; The temperature fitting information and the temperature mapping relationship are integrated through a closed-loop feedback verification mechanism to output a temperature control strategy with the highest matching degree and the most timely response.

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