Multi-dimensional compensated ablation system temperature control system, method, apparatus, and media

Abstract

The present application relates to the technical field of temperature control, and especially relates to an ablation system temperature control system, a method, equipment and a medium with multi-dimensional compensation; the system acquires electrical parameters and ultrasonic image data synchronously, solves medium texture indexes representing changes in the structure of a load medium, constructs an anti-disturbance control model containing an extended state observer, defines energy transmission loss caused by contact thermal resistance as an extended state variable, calculates an adaptive correction factor based on the texture indexes, dynamically corrects the state updating process of the observer to obtain a corrected energy loss estimation value, and constructs a compensation control model to adjust output power accordingly; the present application effectively solves the technical problem of incomplete heating caused by false high sensor temperature and system misjudgment due to deterioration of the contact interface state; precise reconstruction and closed-loop compensation of effective heat flow are realized under a non-ideal contact environment, sufficient energy penetration is maintained even when contact thermal resistance increases dramatically, and the integrity of the ablation area is ensured.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to a temperature control system, method, device and medium for ablation systems with multidimensional compensation. Background Technology

[0002] Ablation techniques, such as radiofrequency ablation (RFA) or microwave ablation (MWA), mainly involve inserting an ablation needle (electrode) into the medium and using the thermal effect to cause coagulation necrosis of the medium. Temperature control is the core of the entire system.

[0003] Existing ablation systems typically employ a closed-loop temperature control strategy. This involves installing temperature sensors such as thermocouples and thermistors inside or on the surface of the ablation needle. The system compares the real-time temperature feedback values ​​collected by the sensors with the set target temperature and automatically adjusts the output power using algorithms such as PID to maintain the temperature within a stable range.

[0004] However, in actual clinical applications, this control method that relies solely on sensor feedback has significant limitations. Existing technologies usually assume that the temperature sensor and the target area maintain an ideal and constant thermal conduction state. Therefore, the sensor's measurement value is directly regarded as the actual temperature of the target area. However, in the actual ablation process, the contact interface between the ablation needle and the medium is not constant, and the thermal conduction efficiency between the two will dynamically fluctuate with the ablation process.

[0005] This change in the thermal conductivity of the contact interface will cause a temperature gradient difference between the temperature of the electrode body where the sensor is located and the actual temperature of the target area. Specifically, when the thermal conductivity decreases, heat is difficult to diffuse outward from the electrode, causing the temperature measured by the sensor to rise rapidly and reach the set threshold. However, the actual temperature of the adjacent area may be far from reaching the ablation temperature. At this time, the existing control system cannot recognize this temperature gradient and will mistakenly judge that the ablation temperature has been reached, thereby reducing or even cutting off the power output. This false standard-reaching phenomenon will cause the volume of the target area to fail to meet the theoretical planning requirements, which can easily cause residue at the edge of the target area. Summary of the Invention

[0006] The main objective of this invention is to provide a multi-dimensional compensation temperature control system for ablation systems, which aims to solve the problem of edge residue caused by the temperature gradient difference between the electrode control temperature and the actual temperature due to changes in the contact interface state in existing temperature control systems.

[0007] To achieve the above objectives, the present invention provides a multi-dimensional compensation temperature control system for an ablation system, the control system comprising:

[0008] The data acquisition module is used to acquire electrical parameters of the ablation electrode output terminal and ultrasonic image data of the target heating area. The electrical parameters include output power and electrode sensor temperature.

[0009] The feature analysis module is used to perform texture analysis on the region of interest in the ultrasound image data to obtain the image grayscale distribution features, and to obtain the medium texture index based on the image grayscale distribution features. The medium texture index is used to characterize the degree of structural change of the load medium in the target heating area during the temperature change process.

[0010] A state observation module is used to construct a disturbance rejection control model. The disturbance rejection control model includes at least one extended state observer. The output power is used as the input of the disturbance rejection control model, and the temperature of the electrode sensor is used as the observation object of the disturbance rejection control model. The energy transmission loss caused by the contact thermal resistance due to the change of the contact interface state is defined as the extended state variable in the extended state observer.

[0011] The compensation calculation module is used to obtain a mapping relationship between the electrode sensor temperature and the medium texture index, and to obtain an adaptive correction factor based on the mapping relationship. The adaptive correction factor is used to quantify the matching deviation between the electrode sensor temperature and the actual heating state of the load medium.

[0012] A compensation correction module is used to correct the update process of the extended state variable according to the adaptive correction factor to obtain a corrected estimate that includes the influence of contact thermal resistance.

[0013] A closed-loop control module is used to construct a compensation control model based on the corrected estimated value and a preset nonlinear error feedback law, generate a power control command based on the compensation control model, and control and adjust the output power of the radio frequency generator according to the power control command to counteract the obstruction of energy conduction by the contact thermal resistance.

[0014] To achieve the above objectives, the present invention also provides a multi-dimensional compensation temperature control method for an ablation system, the control method comprising the following steps:

[0015] Acquire electrical parameters from the output of the ablation electrode and ultrasonic image data of the target heating area. The electrical parameters include output power and electrode sensor temperature.

[0016] Texture analysis is performed on the region of interest in the ultrasound image data to obtain the image grayscale distribution characteristics. Based on the image grayscale distribution characteristics, a medium texture index is obtained. The medium texture index is used to characterize the degree of structural change of the load medium in the target heating area during the temperature change process.

[0017] A disturbance rejection control model is constructed, which includes at least one extended state observer. The output power is used as the input of the disturbance rejection control model and the temperature of the electrode sensor is used as the observation object of the disturbance rejection control model. The energy transfer loss caused by the contact thermal resistance due to the change of the contact interface state is defined as the extended state variable in the extended state observer.

[0018] A mapping relationship is obtained between the electrode sensor temperature and the medium texture index, and an adaptive correction factor is obtained based on the mapping relationship. The adaptive correction factor is used to quantify the matching deviation between the electrode sensor temperature and the actual thermal state of the load medium.

[0019] The update process of the extended state variable is corrected according to the adaptive correction factor to obtain a corrected estimate that includes the effect of contact thermal resistance.

[0020] A compensation control model is constructed based on the corrected estimated value and the preset nonlinear error feedback law. A power control command is generated based on the compensation control model. The output power of the radio frequency generator is controlled and adjusted according to the power control command to counteract the obstruction of energy conduction by the contact thermal resistance.

[0021] To achieve the above objectives, the present invention also provides a computer device including a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program.

[0022] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, wherein a processor executes the computer program.

[0023] The embodiments of the present invention propose,

[0024] This invention synchronously acquires electrical parameters of the ablation electrode output and ultrasonic image data of the target heating area through a data acquisition module. It uses a feature analysis module to calculate the medium texture index, which characterizes the degree of structural change in the load medium. Combined with a state observation module, it constructs an anti-disturbance control model, defining energy transmission loss caused by contact thermal resistance due to changes in the contact interface state as an extended state variable in the extended state observer. Based on a compensation calculation module, it quantifies the matching deviation between the electrode sensor temperature and the actual heating state of the load medium to obtain an adaptive correction factor. The compensation correction module dynamically corrects the update process of the extended state variable. Finally, a closed-loop control module adjusts the output power of the radio frequency generator based on the corrected estimate including the influence of contact thermal resistance and a nonlinear error feedback law. This solves the technical problem in existing ablation technologies where changes in the contact interface state cause contact thermal resistance, resulting in artificially high sensor temperatures that fail to accurately reflect the heating degree of the load medium, leading to system misjudgment and reduced power, resulting in incomplete heating.

[0025] It achieves precise control of effective energy transfer in non-ideal contact environments. It uses the medium texture index as an objective reference for the physical state and uses an adaptive correction factor to correct the observer's estimation logic in real time, enabling it to accurately capture the energy loss shielded by the contact thermal resistance. Furthermore, it actively generates power compensation commands through closed-loop control, driving the RF generator to output power sufficient to offset the obstruction when the contact thermal resistance increases, ensuring that the energy is effectively conducted to the depth of the load medium, and guaranteeing the robustness of temperature control and the uniformity of the ablation effect. Attached Figure Description

[0026] Figure 1 This is a structural block diagram of the control system in Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart illustrating the control method in Embodiment 7 of the present invention.

[0028] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0030] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0031] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0032] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0033] Example 1:

[0034] As attached Figure 1 As shown, this embodiment provides a multi-dimensional compensation temperature control system for an ablation system, the control system comprising:

[0035] The data acquisition module is used to acquire electrical parameters of the ablation electrode output terminal and ultrasonic image data of the target heating area. The electrical parameters include output power and electrode sensor temperature.

[0036] The feature analysis module is used to perform texture analysis on the region of interest in the ultrasound image data to obtain the image grayscale distribution features, and to obtain the medium texture index based on the image grayscale distribution features. The medium texture index is used to characterize the degree of structural change of the load medium in the target heating area during the temperature change process.

[0037] A state observation module is used to construct a disturbance rejection control model. The disturbance rejection control model includes at least one extended state observer. The output power is used as the input of the disturbance rejection control model, and the temperature of the electrode sensor is used as the observation object of the disturbance rejection control model. The energy transmission loss caused by the contact thermal resistance due to the change of the contact interface state is defined as the extended state variable in the extended state observer.

[0038] The compensation calculation module is used to obtain a mapping relationship between the electrode sensor temperature and the medium texture index, and to obtain an adaptive correction factor based on the mapping relationship. The adaptive correction factor is used to quantify the matching deviation between the electrode sensor temperature and the actual heating state of the load medium.

[0039] A compensation correction module is used to correct the update process of the extended state variable according to the adaptive correction factor to obtain a corrected estimate that includes the influence of contact thermal resistance.

[0040] A closed-loop control module is used to construct a compensation control model based on the corrected estimated value and a preset nonlinear error feedback law, generate a power control command based on the compensation control model, and control and adjust the output power of the radio frequency generator according to the power control command to counteract the obstruction of energy conduction by the contact thermal resistance.

[0041] In existing ablation systems, the thermal conductivity of the contact interface between the ablation needle and the target area fluctuates dynamically during the ablation process, resulting in a non-negligible temperature gradient difference between the temperature measured by the electrode sensor and the actual temperature of the surrounding medium. Specifically, when the contact thermal resistance increases due to medium dehydration or carbonization, heat is difficult to dissipate from the electrode body, causing the sensor temperature to rise rapidly and reach the set threshold. However, the actual temperature of the adjacent medium may be far below the lethal dose required for coagulative necrosis. Existing control systems, unable to recognize this temperature gradient, may misjudge that the ablation temperature has been reached, thus reducing power output. This results in the ablation lesion volume failing to meet preoperative planning requirements, ultimately affecting the safety and effectiveness of the temperature control process.

[0042] Based on the above problems, this embodiment provides a multi-dimensional compensation ablation system temperature control system. The control system introduces a multi-source information fusion mechanism, adding a multi-physical state observation dimension based on acoustic images in addition to the traditional feedback loop, thereby constructing a multi-dimensional compensation closed-loop control architecture that can sense and compensate for changes in contact thermal resistance in real time.

[0043] Specifically, the control system first performs the underlying signal acquisition task through the data acquisition module. During system operation, the data acquisition module monitors the electrical parameters at the output end of the ablation electrode in real time through the high-frequency sampling circuit. The output power reflects the total energy injected into the load medium by the system, while the electrode sensor temperature reflects the thermal state at the energy transmission starting point. At the same time, by synchronously acquiring ultrasonic image data covering the target heating area, it provides raw morphological information for subsequent non-contact condition assessment.

[0044] After acquiring the raw data, the feature analysis module performs texture analysis on the region of interest (ROI) in the ultrasound image data. Because the acoustic impedance characteristics of the loading medium change during temperature variations (e.g., protein denaturation, dehydration shrinkage, or microbubble generation due to heat), the grayscale distribution of the ultrasound echoes on the image exhibits specific statistical patterns. To quantify the degree of this structural change, this system uses the Gray-Level Co-occurrence Matrix (GLCM) as the basic tool for texture feature extraction. The feature analysis module first calculates the GLCM of the RIO, then extracts energy and entropy feature values ​​from it, and calculates the medium texture index through a weighted combination. The calculation of the medium texture index satisfies Expression 1: In the formula, The dimensionless index represents the medium texture index at time k, used to quantify the degree of physical change in the internal structure of the loaded medium. The data comes from the calculation output of the feature analysis module.

[0045] The normalized probability of gray level i and gray level j appearing simultaneously in the gray-level co-occurrence matrix is ​​derived from the statistical results of gray-level co-occurrence matrix transformation of the region of interest in the real-time acquired ultrasound image. The weighting coefficient representing the energy feature is calibrated based on offline experimental data and is used to adjust the proportion of texture consistency features in the comprehensive index. The weighting coefficient representing the entropy feature is also calibrated based on offline experimental data and is used to adjust the proportion of texture randomness features in the comprehensive index.

[0046] In the above expression, a weighted combination of energy and entropy is used because these two features are complementary in describing the structural changes of the medium under heat. The first term corresponds to the energy feature, reflecting the uniformity and regularity of the texture; the second term corresponds to the entropy feature, reflecting the complexity and disorder of the texture. During the heating process, as the load medium undergoes structural changes (such as solidification or vaporization), the image texture usually becomes coarser and more disordered, leading to an increase in entropy and a decrease in energy. Through the weighted processing in the above expression, this microscopic texture change can be mapped to a monotonically changing numerical index, thus providing the control system with a physical fact reference independent of the temperature sensor, effectively solving the problem that temperature readings alone cannot determine whether the medium is truly heated.

[0047] After obtaining the texture index characterizing the physical state, the state observation module constructs a disturbance rejection control model to perform in-depth analysis of the system's internal state. Traditional PID control is difficult to handle nonlinear time-varying disturbances caused by changes in the contact interface state (such as gas film formation). Therefore, this system introduces the Extended State Observer (ESO) from the Active Disturbance Rejection Control theory. By using the output power as the input excitation of the model and the electrode sensor temperature as the observation object, a third-order state-space model including temperature state, temperature change rate, and energy transfer loss is established. The most crucial aspect is that the energy transfer loss caused by contact thermal resistance is defined as the extended state variable, i.e., the total disturbance estimate. The discrete iterative update process of the Extended State Observer satisfies the following expression: In the formula, This represents the estimated temperature state at time k, in degrees Celsius, derived from iterative calculations of the observer's internal state variables; This represents the estimated rate of temperature change at time k, in degrees Celsius per second, derived from iterative calculations of the observer's internal state variables; This represents the extended state variable at time k, which is the total disturbance estimate including the influence of contact thermal resistance, derived from the iterative calculation of the internal state variables of the observer; This represents the measured temperature of the electrode sensor at time k, in degrees Celsius, and is derived from the data acquisition module. The output power at time k is represented in watts and is derived from the data acquisition module. This represents the observation error, which is the difference between the estimated temperature and the measured temperature, and is derived from real-time calculations.

[0048] h This indicates the system's sampling step size, which is determined by the system clock setting. , , All of these represent the observer gain coefficients, derived from the parameter tuning results based on the bandwidth method; This represents the estimated control gain of the system, reflecting the nominal influence of power on temperature changes, and is derived from the physical modeling parameters of the system. This represents the adaptive correction factor at time k, which originates from the output of the compensation calculation module.

[0049] Understandably, the above expression 2 constitutes the core architecture of the observer. The first two equations track the system temperature and its rate of change, while the third equation captures the total disturbance in real time.

[0050] In conventional designs, the observer gain is fixed, which results in a slow response to sudden changes in contact thermal resistance (such as instantaneous blockage of heat flow by a gas film). An adaptive correction factor is introduced into the perturbation update stage. As a dynamic weight, the system can force the observer's sensitivity to errors based on externally perceived physical facts (texture index). For example, when the texture index indicates that the medium is not heated but the sensor temperature is very high... It will increase significantly, forcing It converges rapidly in the negative direction, thus accurately estimating the huge energy loss caused by contact thermal resistance.

[0051] In order to generate the aforementioned correction factor, the compensation calculation module performs a logical judgment task. This module establishes a mapping relationship between the electrode sensor temperature and the medium texture index, aiming to quantify the deviation between the reading and the actual result.

[0052] The mapping calculation process of the adaptive correction factor satisfies the following expression three: In the formula, This represents the fusion gain limit, used to limit the maximum value of the correction magnitude, and is derived from a system-preset empirical constant.

[0053] tanh represents the hyperbolic tangent function, used to provide smooth transition characteristics for nonlinearity;

[0054] h represents the sampling step size; This indicates the target control temperature, which is derived from user settings or system planning. This indicates the current medium texture index, which originates from the feature analysis module; This represents the texture feature normalization coefficient, used to map the texture index to an order of magnitude that matches the temperature deviation, and is derived from offline calibration; ε It represents a very small positive number to prevent the denominator from being zero.

[0055] It should be noted that the above expression three designs a highly adaptive contradiction detection mechanism. The numerator represents the temperature deviation sensed by the sensor, and the denominator represents the actual structural change of the medium. Under normal heating conditions, as the temperature approaches the target value (numerator decreases), the medium structure changes (denominator increases; assuming the texture index monotonically increases with structural change), and the value of the entire fraction tends to zero. The output of the tanh function approaches zero. ≈1, the system maintains the normal observation mode. However, in special scenarios with poor contact, if the sensor temperature has risen or even exceeded the target (numerator is large), but the medium texture index is still very low (denominator is small, indicating that the medium structure has not changed), the value of the fraction will increase sharply, and the tanh function will tend to 1, leading to... Rapidly increased to 1+ This design logic can keenly detect the phenomenon of false high temperature, that is, the sensor gets hot but the medium does not, thereby triggering the downstream high-gain compensation mechanism.

[0056] Finally, the closed-loop control module generates the final command based on the corrected state estimate, utilizing the corrected estimate. A compensation control model is constructed using a preset nonlinear error feedback law. The calculation process of the power control command satisfies the following expression four: ;of which the nominal control component The calculation is performed using a nonlinear error feedback law, satisfying the following expression five: ; This indicates the final power control command, expressed in watts or duty cycle, used to directly drive the RF generator; This represents the nominal control component, derived from nonlinear feedback calculations of temperature error and rate of change. It also indicates that the corrected extended state variables (total disturbance estimate) are derived from the compensated and corrected output of the state observation module. This represents a nonlinear control function with the characteristics of large gain with small error and small gain with large error, and it originates from the active disturbance rejection control algorithm library.

[0057] , Both represent nonlinear factors, which determine the degree of nonlinearity of the feedback law and originate from parameter tuning.

[0058] δ represents the width of the linear interval, which is derived from parameter tuning.

[0059] In expressions four and five above, there is actually a feedforward compensation mechanism. When the system detects a large contact thermal resistance, the state observation module outputs... This will manifest as a large negative value (representing energy loss); subtracting this negative value from the control law is equivalent to adjusting the nominal control quantity. A positive power compensation component is superimposed on top of this; this means that even if the sensor temperature is already very high, as long as the algorithm determines that there is contact thermal resistance loss (i.e., Even if the output power is negative, the system will still maintain or even increase its output power to penetrate the barrier formed by the contact thermal resistance and ensure that energy can be effectively transferred to the depths of the load medium, thereby fundamentally solving the problem of incomplete heating caused by changes in the contact interface state.

[0060] In summary, the multi-dimensional compensation ablation system temperature control system provided in this embodiment constructs an extended state observer based on adaptive texture feedback enhancement by synchronously acquiring electrical parameters and ultrasonic texture features. It uses the medium texture index as a physical truth reference to dynamically correct the observer's observation gain for contact thermal resistance disturbances and implements nonlinear feedforward compensation based on the corrected total disturbance estimate. This solves the technical problem in existing ablation systems where, due to excessively high sensor temperatures, the medium's heating state is misjudged when contact thermal resistance increases due to medium dehydration or vaporization, leading to premature reduction in output power and incomplete heating of the target area. It achieves accurate reconstruction and closed-loop control of effective heat flow in complex contact environments, ensuring sufficient energy penetration even under extreme conditions where heat conduction at the electrode-medium interface is obstructed, thereby guaranteeing the integrity of the ablation area.

[0061] Example 2:

[0062] In this embodiment, the feature analysis module includes:

[0063] A matrix construction unit is used to obtain the gray-level co-occurrence matrix of the region of interest.

[0064] The feature extraction unit is used to extract feature parameters reflecting the roughness and complexity of image texture from the gray-level co-occurrence matrix. The feature parameters include energy feature values ​​and entropy feature values.

[0065] An index generation unit is used to combine the energy feature value and the entropy feature value to obtain the medium texture index.

[0066] Understandably, after the data acquisition module obtains the ultrasound image data of the target heating area, the matrix construction unit is the first to start working. Since the original ultrasound image is only a set of two-dimensional gray-level numerical matrices, the simple pixel gray-level values ​​are difficult to reflect the spatial structure information inside the medium. Therefore, the core task of the matrix construction unit is to construct a gray-level co-occurrence matrix that can describe the spatial correlation of pixels.

[0067] It is also understandable that this unit first identifies the region of interest in the image and performs dimensionality reduction quantization on the pixel gray levels within that region, typically compressing the gray levels to a preset range (e.g., 16 or 64 levels) to reduce computational load and suppress speckle noise interference. Subsequently, the matrix construction unit, based on a preset displacement vector (including distance d and direction θ), counts the frequency of two pixels within the region simultaneously presenting a specific gray level pair while maintaining this displacement relationship. Specifically, it counts the number of times a pixel with gray level i appears in pairs with a pixel with gray level j at distance d and direction θ, and normalizes this statistical result to finally generate a probability matrix describing the texture spatial dependency. This process transforms the disordered gray level information of the image into ordered texture statistical information, laying the data foundation for subsequent feature solving.

[0068] After obtaining the standardized gray-level co-occurrence matrix, the feature extraction unit begins its calculations. When the loading medium (such as gel, biomaterial simulants, etc.) undergoes structural changes upon heating (e.g., from a liquid gel to a solid condensate, or the generation of microbubbles), the uniformity and randomness of its acoustic impedance distribution change significantly. To capture this change, the feature extraction unit selectively extracts two key feature parameters from the gray-level co-occurrence matrix that reflect the roughness and complexity of the image texture: energy eigenvalues ​​and entropy eigenvalues. The energy eigenvalue is obtained by calculating the sum of squares of the elements in the matrix, measuring the uniformity and repeatability of the image texture; while the entropy eigenvalue is obtained by calculating the sum of the logarithmic products of the matrix elements, measuring the randomness and information content of the image texture. During structural phase transitions or changes in the loading medium, the originally uniform medium often becomes rough and chaotic due to increased discontinuities in its microstructure, statistically manifested as a decrease in energy eigenvalues ​​and an increase in entropy eigenvalues.

[0069] In order to integrate the two feature parameters with different trends into a unified index that can monotonically represent the degree of change in the medium structure, the index generation unit performed a key data fusion operation. By adopting a nonlinear weighted combination algorithm logic, the medium texture index was calculated. The design goal of this index is to establish an observation variable that monotonically increases with the degree of change in the medium structure (such as solidification depth) so that subsequent control links can perform linearization processing or threshold judgment.

[0070] The specific calculation process is a variation of expression one, satisfying: In the formula, This indicates the total number of gray levels after image quantization (e.g., 64 or 256).

[0071] In the above expression, the energy characteristics are processed by reciprocal. In the physical process, when the load medium is not heated or the structure is uniform, the image texture is delicate, the energy value of the gray-level co-occurrence matrix is ​​large and close to 1, while the entropy value is small.

[0072] As heating proceeds, structural changes occur within the medium (such as dehydration, vaporization, or solidification), and the texture becomes rough and disordered, resulting in a decrease in energy value and an increase in entropy value. If energy and entropy are directly added together, their changes are in opposite directions (one decreases and the other increases), which will cancel each other out, leading to a decrease in the sensitivity of the final exponent to structural changes. Therefore, this embodiment adopts a strategy of adding the reciprocal energy term to the entropy term.

[0073] As the medium structure becomes more complex, the energy value decreases, while its reciprocal term increases; simultaneously, the entropy term also increases, and the two exhibit a mutually reinforcing effect, resulting in... It can extremely sensitively capture subtle changes in the microstructure of the medium and has a very high signal-to-noise ratio. This ensures that the medium texture index maintains a strict monotonically positive correlation with the degree of structural change of the loaded medium. Regardless of whether the texture change is caused by bubble formation or density change, the index will rise steadily, thus providing a physical truth reference with good linearity and strong robustness for the subsequent state observation module, avoiding control logic confusion caused by inconsistent changes in characteristic parameters.

[0074] Example 3:

[0075] In this embodiment, the state observation module includes:

[0076] The system construction unit is used to establish the state variable system of the disturbance rejection control model. The state variable system includes the temperature state estimate, the temperature change rate estimate, and the energy loss estimate as an extended state variable.

[0077] The tracking configuration unit is used to configure the extended state observer gain so that the extended state observer tracks the temperature change of the electrode sensor according to the output power, and incorporates the disturbances of the internal parameters of the disturbance rejection control model and the changes in the thermal conduction environment into the energy loss estimate.

[0078] It should be noted that in traditional temperature control, the system usually only focuses on the current temperature value, while ignoring the dynamic trend of temperature change and the nonlinear deviation between energy input and temperature response. To this end, the system building unit abstracts the controlled object (i.e. the heating process of the load medium) into a second-order dynamic system, and on this basis, introduces extended state variables to construct a third-order state space.

[0079] Specifically, this state variable system comprises three core components: a temperature state estimate, characterizing the theoretical temperature of the system at the current moment; a temperature change rate estimate, characterizing the instantaneous rate of temperature rise or fall (i.e., the first derivative of temperature); and an energy loss estimate, which is a virtual physical quantity introduced as an extended state variable. Physically, the energy loss estimate does not directly correspond to a specific physical component parameter, but rather serves as a total perturbation container, absorbing and characterizing all factors that cause the actual temperature response of the system to deviate from theoretical expectations. The most significant component is the energy transfer loss caused by contact thermal resistance resulting from changes in the contact interface state (such as film formation and physical gap generation), while also including the effects of model parameter perturbations and changes in external environmental heat conduction.

[0080] After establishing the state variable system, the tracking configuration unit is responsible for configuring the operating parameters and iterative logic of the extended state observer, enabling it to operate in a closed loop. It not only uses the output power obtained by the data acquisition module as the system's control input (excitation signal) and the temperature of the electrode sensor as the system's observation output (feedback signal), but more importantly, it can also receive dynamic weight signals from the compensation and correction module to achieve online adjustment of the observation sensitivity. By comparing the applied energy with the generated temperature in real time and combining the dynamic weight signals, the observer uses a weighted error feedback mechanism to force the internal state variables to approximate the real physical process.

[0081] Example 4:

[0082] The compensation calculation module includes:

[0083] A deviation acquisition unit is used to acquire the absolute deviation between the electrode sensor temperature and the set target temperature;

[0084] A factor adjustment unit is configured to obtain an adaptive correction factor based on the mapping relationship, wherein the mapping relationship is specifically configured as follows:

[0085] The current medium texture index is detected. When the absolute deviation exceeds the preset range and the medium texture index indicates that the load medium has not undergone structural changes, it is determined that there is contact thermal resistance shielding and the value of the adaptive correction factor is increased.

[0086] When the medium texture index indicates a structural change in the load medium or the electrode sensor temperature reaches the target temperature, the value of the adaptive correction factor is maintained.

[0087] Understandably, once the system enters the real-time control loop, the deviation acquisition unit first performs basic data processing tasks, reading the current electrode sensor temperature from the data acquisition module and retrieving the set target temperature from the system's preset control parameters. Although traditional PID control also calculates temperature deviation, in this embodiment, the deviation acquisition unit focuses not on the simple control error sign, but on the absolute magnitude of the deviation, as this magnitude represents the degree of energy accumulation at the transmission starting point. By calculating the absolute difference between the electrode sensor temperature and the set target temperature in real time, an absolute temperature deviation signal is generated, directly reflecting the degree of deviation of the thermal state of the electrode body. If this deviation value is too large, it usually means that energy has accumulated or dissipated abnormally at the electrode. However, this single indicator is insufficient to determine whether it is overheating or heat conduction obstruction; therefore, it is necessary to combine it with the state information of the physical medium for further judgment.

[0088] Based on this, the factor adjustment unit performs the core mapping calculation and logical judgment. This unit receives the medium texture index from the feature analysis module and the absolute temperature deviation from the deviation acquisition unit, and substitutes both into a preset nonlinear mapping model for fusion calculation. In an ideal low thermal resistance contact environment, the increase in electrode temperature should be accompanied by a synchronous change in the load medium structure (i.e., a significant change in the texture index); conversely, if the electrode temperature is very high (large deviation) but the load medium structure remains unchanged (low texture index), it indicates that there is a physical barrier that blocks heat flow (i.e., contact thermal resistance shielding). In order to transform this qualitative logical judgment into a quantitative control signal, the factor adjustment unit adopts an adaptive mapping algorithm based on the hyperbolic tangent function to calculate the adaptive correction factor.

[0089] It is also understandable that the nonlinear characteristics of the tanh function introduced in Expression 3 of Example 1 make the change of the correction factor continuous and smooth, avoiding the control signal jump that may be caused by using hard threshold judgment (such as If-Else logic). This design enables the control system to achieve seamless switching, which not only ensures the control accuracy in steady state, but also gives the system the dynamic characteristic of instantaneous burst compensation capability when encountering contact deterioration, thereby ensuring that energy can penetrate the high thermal resistance interface and achieve effective action on the load medium.

[0090] Example 5:

[0091] The compensation and correction module includes:

[0092] A gain weighting unit is used to apply the adaptive correction factor as a dynamic weight to the observation error feedback term of the extended state observer in order to adjust the response sensitivity of the extended state observer to the observation error.

[0093] A state iteration unit is used to perform iterative update calculations of extended state variables based on the adjusted response sensitivity, and to use the output of the updated extended state variables as the corrected estimate including the influence of contact thermal resistance.

[0094] It should be noted that after constructing an adaptive correction factor capable of sensing the physical contact state, the system's control core enters the critical execution phase, namely, using this factor to substantially intervene in the internal evolution process of the observer. This module is located at the connection hub between adaptive calculation and closed-loop control. Through the close cooperation between the gain weighting unit and the state iteration unit, it breaks the limitation of the traditional extended state observer having a constant gain, giving the system the ability to dynamically adjust the observation bandwidth under different contact thermal resistance conditions, thereby ensuring that the output energy loss estimate can reflect the current physical true value without lag.

[0095] Specifically, the gain weighting unit first receives the adaptive correction factor from the compensation calculation module and applies it as a dynamic weight to the observation error feedback term of the extended state observer. In a conventional extended state observer (ESO) design, the observer uses the observation error between the model output temperature and the measured temperature to drive the update of the state variables. The driving force is usually determined by a set of fixed gain coefficients.

[0096] However, a fixed gain means that the observer's response speed to disturbances (i.e., the observation bandwidth) is constant. When encountering a sudden change in contact thermal resistance caused by a gas film or gap, a huge deviation will quickly arise between the measured temperature and the model's predicted temperature because the heat flow is blocked. If the conventional gain is still used for correction, the observer often needs a long time integration to interpret this deviation as energy loss, which will lead to a serious lag in the control signal. To address this, the gain weighting unit introduces a dynamic weighting mechanism, which directly uses an adaptive correction factor to perform online multiplicative correction on the feedback gain acting on the extended state variable (i.e., the total disturbance term), thereby mathematically realizing an instantaneous amplification of the observation sensitivity.

[0097] Subsequently, the state iteration unit performs iterative update calculations of the extended state variables based on the adjusted response sensitivity. According to the difference equation in discrete time, it uses the state value of the previous moment, the current observation error, and the weighted gain to calculate the value of the extended state variables at the next moment. This process is not only the accumulation of values, but also the reconstruction of physical information. Through iterative calculation, temperature deviations that cannot be explained by the nominal physical model are forcibly classified as energy transmission losses and finally output to the closed-loop control module in the form of corrected estimates.

[0098] Example 6:

[0099] The closed-loop control module includes:

[0100] The component calculation unit is used to calculate the error between the set target temperature and the current temperature state estimate, and to process the error according to a preset nonlinear error feedback law to obtain the nominal control component.

[0101] The instruction generation unit is used to subtract the corrected estimate from the nominal control component and divide the calculation result by the system gain estimate to generate the power control instruction.

[0102] Understandably, the component calculation unit first performs error-based control law calculation. This unit receives the temperature state estimate and temperature change rate estimate from the state observation module and compares them with the system's preset target temperature. In conventional PID control, error handling is usually linear, but in ablation scenarios involving heat conduction, the temperature response of the load medium often exhibits large hysteresis and nonlinearity. To improve the dynamic response quality of the control system, the component calculation unit employs a preset nonlinear error feedback law. The core idea of ​​this feedback law is to use a larger gain when the error is large to accelerate the response speed, and a smaller gain when the error is small to prevent overshoot and oscillation, thereby calculating an ideal control quantity that does not contain disturbance effects, i.e., the nominal control component.

[0103] Subsequently, the command generation unit performs the most critical disturbance compensation operation. By receiving the corrected estimate (i.e., the extended state variable that includes the influence of contact thermal resistance) output from the compensation correction module and the nominal control component output from the component calculation unit, the command generation unit subtracts the corrected estimate from the nominal control component according to the separation-compensation principle of active disturbance rejection control, and divides the result by the system gain estimate, thereby restoring the final power control command. This step is physically equivalent to: adding an extra part of power on top of the nominal power required to maintain ideal temperature control, in order to offset the energy loss identified by the observer, thereby ensuring that the effective energy actually acting on the load medium is consistent with the theoretical expectation.

[0104] Example 7:

[0105] As attached Figure 2 As shown, this embodiment provides a multi-dimensional compensation temperature control method for an ablation system, the control method including the following steps:

[0106] Acquire electrical parameters from the output of the ablation electrode and ultrasonic image data of the target heating area. The electrical parameters include output power and electrode sensor temperature.

[0107] Texture analysis is performed on the region of interest in the ultrasound image data to obtain the image grayscale distribution characteristics. Based on the image grayscale distribution characteristics, a medium texture index is obtained. The medium texture index is used to characterize the degree of structural change of the load medium in the target heating area during the temperature change process.

[0108] A disturbance rejection control model is constructed, which includes at least one extended state observer. The output power is used as the input of the disturbance rejection control model and the temperature of the electrode sensor is used as the observation object of the disturbance rejection control model. The energy transfer loss caused by the contact thermal resistance due to the change of the contact interface state is defined as the extended state variable in the extended state observer.

[0109] A mapping relationship is obtained between the electrode sensor temperature and the medium texture index, and an adaptive correction factor is obtained based on the mapping relationship. The adaptive correction factor is used to quantify the matching deviation between the electrode sensor temperature and the actual thermal state of the load medium.

[0110] The update process of the extended state variable is corrected according to the adaptive correction factor to obtain a corrected estimate that includes the effect of contact thermal resistance.

[0111] A compensation control model is constructed based on the corrected estimated value and the preset nonlinear error feedback law. A power control command is generated based on the compensation control model. The output power of the radio frequency generator is controlled and adjusted according to the power control command to counteract the obstruction of energy conduction by the contact thermal resistance.

[0112] In this embodiment, the step of correcting the update process of the extended state variable according to the adaptive correction factor specifically includes:

[0113] The adaptive correction factor is applied as a dynamic weight to the observation error feedback term of the extended state observer to adjust the response sensitivity of the extended state observer to the observation error.

[0114] Based on the adjusted response sensitivity, perform iterative update calculations of the extended state variables, and use the output of the updated extended state variables as the corrected estimate including the influence of contact thermal resistance.

[0115] It should be noted that the control method performs a multi-source data synchronous acquisition step. In this stage, the system acquires electrical parameters from the ablation electrode output and ultrasound image data of the target heating area in parallel. The electrical parameters (including output power and electrode sensor temperature) reflect the input and feedback port status of the energy transmission system, while the ultrasound image data provides non-contact tomographic information of the internal structure of the load medium. The two are strictly synchronized on the time axis, laying the foundation for subsequent information fusion.

[0116] Subsequently, the method enters the feature analysis stage. The system performs texture analysis on the region of interest in the ultrasound image. By analyzing the statistical characteristics of the gray-scale distribution of the image, the medium texture index is calculated. This index, as a quantitative physical state indicator, is used to characterize the degree of structural change of the load medium during temperature change. In a physical sense, it is used to verify the authenticity and effectiveness of the temperature change of the electrode sensor.

[0117] Meanwhile, the control method constructs an anti-disturbance control model at the algorithm level. The core of this model is to run an extended state observer, which not only uses the output power as the excitation input of the system and the temperature of the electrode sensor as the observation output of the system, but more importantly, it also presets an extended state variable in the mathematical model. This variable is used to characterize the energy transmission loss caused by the contact thermal resistance due to changes in the contact interface state (such as the formation of an air film or the generation of gaps). This enables the control system to theoretically accommodate and quantify energy leakage.

[0118] To accurately capture the aforementioned energy loss, the control method performs a mapping calculation step. The system establishes a logical mapping based on the electrode sensor temperature and the medium texture index, and calculates an adaptive correction factor. This step aims to quantify the matching deviation between the sensor reading and the actual heating state of the load medium. When the sensor displays a high temperature but the medium texture shows no heating, the factor will identify this mismatch and mark it as a high-risk contact thermal resistance shielding state.

[0119] Based on the above calculation results, the method performs dynamic correction and update steps for the extended state variables. Specifically, the system applies an adaptive correction factor as a dynamic weight to the observation error feedback term of the extended state observer. Logically, this means that the system adjusts the observer's sensitivity or response sensitivity to temperature deviations in real time according to the current physical contact state. Subsequently, based on the adjusted sensitivity, the algorithm performs iterative update calculations and outputs corrected estimates that include the influence of contact thermal resistance. Through this process, temperature deviations that cannot be explained by normal heat conduction are forcibly attributed to energy loss, thereby achieving the numerical reconstruction of physical faults in the control variables.

[0120] Finally, the control method performs closed-loop compensation and execution steps, constructs a compensation control model based on the corrected estimated value and the preset nonlinear error feedback law, and generates the final power control command. This command logically includes a tracking component for the target temperature and a compensation component for energy loss. By adjusting the output power of the RF generator, the system can actively counteract the obstruction of energy conduction by the contact thermal resistance, ensuring that energy can still effectively penetrate the interface and act on the load medium in complex contact environments, maintaining the continuity and effectiveness of the heating process.

[0121] Furthermore, in one embodiment, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the methods described in the foregoing embodiments.

[0122] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0123] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0124] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0125] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0126] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0127] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0129] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A temperature control system for a multi-dimensional compensated ablation system, characterized in that, The control system includes: The data acquisition module is used to acquire electrical parameters of the ablation electrode output terminal and ultrasonic image data of the target heating area. The electrical parameters include output power and electrode sensor temperature. The feature analysis module is used to perform texture analysis on the region of interest in the ultrasound image data to obtain the image grayscale distribution features, and to obtain the medium texture index based on the image grayscale distribution features. The medium texture index is used to characterize the degree of structural change of the load medium in the target heating area during the temperature change process. A state observation module is used to construct a disturbance rejection control model. This model includes at least one extended state observer. The output power is used as the input to the model, and the electrode sensor temperature is used as the observed object. The energy transfer loss caused by contact thermal resistance due to changes in the contact interface state is defined as the extended state variable in the extended state observer. A third-order state-space model is established, including temperature state, temperature change rate, and energy transfer loss. The discrete iterative update process of the extended state observer satisfies the following: ； In the formula, This represents the estimated temperature state at time k. This represents the estimated rate of temperature change at time k. Represents the extended state variable at time k; This represents the measured value of the electrode sensor temperature at time k. This represents the output power at time k; This represents the observation error, specifically the difference between the estimated temperature and the measured temperature. h Indicates the sampling step size of the system; , , Both represent the observer gain coefficient; This represents the estimated value of the system control gain; This represents the adaptive correction factor at time k; The compensation calculation module is used to obtain a mapping relationship between the electrode sensor temperature and the medium texture index, and to obtain an adaptive correction factor based on the mapping relationship. The adaptive correction factor is used to quantify the matching deviation between the electrode sensor temperature and the actual heating state of the load medium. A compensation correction module is used to correct the update process of the extended state variable according to the adaptive correction factor to obtain a corrected estimate that includes the influence of contact thermal resistance. A closed-loop control module is used to construct a compensation control model based on the corrected estimated value and a preset nonlinear error feedback law, generate a power control command based on the compensation control model, and control and adjust the output power of the radio frequency generator according to the power control command to counteract the obstruction of energy conduction by the contact thermal resistance.

2. The temperature control system for the multi-dimensional compensation ablation system as described in claim 1, characterized in that, The feature analysis module includes: A matrix construction unit is used to obtain the gray-level co-occurrence matrix of the region of interest. The feature extraction unit is used to extract feature parameters reflecting the roughness and complexity of image texture from the gray-level co-occurrence matrix. The feature parameters include energy feature values ​​and entropy feature values. An index generation unit is used to combine the energy feature value and the entropy feature value to obtain the medium texture index.

3. The temperature control system for the multi-dimensional compensation ablation system as described in claim 1, characterized in that, The state observation module includes: The system construction unit is used to establish the state variable system of the disturbance rejection control model. The state variable system includes the temperature state estimate, the temperature change rate estimate, and the energy loss estimate as an extended state variable. The tracking configuration unit is used to configure the extended state observer gain so that the extended state observer tracks the temperature change of the electrode sensor according to the output power, and incorporates the disturbances of the internal parameters of the disturbance rejection control model and the changes in the thermal conduction environment into the energy loss estimate.

4. The temperature control system for the multi-dimensional compensation ablation system as described in claim 1, characterized in that, The compensation calculation module includes: A deviation acquisition unit is used to acquire the absolute deviation between the electrode sensor temperature and the set target temperature; A factor adjustment unit is configured to obtain an adaptive correction factor based on the mapping relationship, wherein the mapping relationship is specifically configured as follows: The current medium texture index is detected. When the absolute deviation exceeds the preset range and the medium texture index indicates that the load medium has not undergone structural changes, it is determined that there is contact thermal resistance shielding and the value of the adaptive correction factor is increased. When the medium texture index indicates a structural change in the load medium or the electrode sensor temperature reaches the target temperature, the value of the adaptive correction factor is maintained.

5. The temperature control system for the multi-dimensional compensation ablation system as described in claim 1, characterized in that, The compensation and correction module includes: A gain weighting unit is used to apply the adaptive correction factor as a dynamic weight to the observation error feedback term of the extended state observer in order to adjust the response sensitivity of the extended state observer to the observation error. A state iteration unit is used to perform iterative update calculations of extended state variables based on the adjusted response sensitivity, and to use the output of the updated extended state variables as the corrected estimate including the influence of contact thermal resistance.

6. The temperature control system for the multi-dimensional compensation ablation system as described in claim 1, characterized in that, The closed-loop control module includes: The component calculation unit is used to calculate the error between the set target temperature and the current temperature state estimate, and to process the error according to a preset nonlinear error feedback law to obtain the nominal control component. The instruction generation unit is used to subtract the corrected estimate from the nominal control component and divide the calculation result by the system gain estimate to generate the power control instruction.

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