Ablation process data management system and method based on big data evaluation

Abstract

The present invention relates to the field of monitoring data analysis technology, and in particular to an ablation process data management system and method based on big data evaluation, comprising: analyzing the abnormal blood flow state during the ablation of varicose veins in the patient's lower limbs through blood flow state data and vascular structure data in the ablation area; analyzing the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs through temperature field data and vascular structure data in the ablation area; quantifying the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs based on the analysis results of the abnormal blood flow state and the analysis results of the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs; and dynamically adjusting the parameters of the ablation device based on the quantified results of the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs; thereby improving the success rate of the operation, reducing the surgical risk, and improving the patient's postoperative quality of life.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring data analysis, and in particular to an ablation process data management system and method based on big data evaluation. Background Art

[0002] Varicose veins of the lower limbs are a common vascular disease characterized by abnormal dilation and tortuosity of the lower limb veins. This condition not only severely impacts aesthetics but can also cause a range of symptoms, including pain, swelling, and even skin ulcers, significantly reducing patients' quality of life. Traditional treatments for varicose veins, such as medication, pressure therapy, and surgery, while effective, are often associated with significant trauma, slow recovery, and high recurrence rates. With the rapid advancement of medical technology, minimally invasive therapies are emerging as a new approach to treating varicose veins. Microwave ablation, among other techniques, has garnered significant attention due to its unique advantages. Microwave ablation utilizes microwave energy to heat the diseased vessels, causing the denaturation and coagulation of proteins in the vessel wall, ultimately occluding the vessels.

[0003] However, existing technologies often focus on microwave ablation of solid tumors in other general locations. These tumors differ significantly from varicose veins of the lower extremities in terms of anatomical structure, physiological function, and hemodynamic characteristics. Due to the lack of optimization specifically for varicose veins of the lower extremities, existing technologies, when applied to the treatment of varicose veins of the lower extremities, fail to fully consider the essential differences between ablation of solid tumors in general locations and vascular ablation. There is a lack of comprehensive monitoring and dynamic adjustment of blood flow status, vascular structure, device parameters, and temperature field data during the ablation process. This results in incomplete or excessive ablation, increases surgical risks, and affects the ultimate ablation effect.

[0004] In order to solve these problems, this application designs an ablation process data management system and method based on big data evaluation. Summary of the Invention

[0005] The present invention aims to provide an ablation process data management system and method based on big data assessment. By constructing models for analyzing abnormal blood flow, damage to the ablation area, and dynamic surgical risk quantification, various risks during surgery can be assessed and quantified in real time. Based on the results of the dynamic surgical risk quantification, this solution can dynamically adjust ablation device parameters, such as ablation temperature and ablation speed, to ensure the safety and effectiveness of the surgery. This approach not only improves the success rate of the surgery, reduces surgical risks, but also improves the patient's postoperative quality of life.

[0006] The present invention is achieved in that:

[0007] In a first aspect, the present invention provides a method for managing ablation process data based on big data evaluation, comprising the following steps:

[0008] S1. Obtain blood flow status data and vascular structure data in the ablation area during the ablation of varicose veins in the patient's lower limbs, and simultaneously obtain temperature field data in the ablation area;

[0009] S2. Based on the blood flow status data and vascular structure data of the ablation area, a blood flow abnormality analysis model is constructed to analyze the abnormal blood flow status during the ablation of varicose veins in the patient's lower limbs;

[0010] S3. Based on the temperature field data and vascular structure data of the ablation area, a damage state analysis model of the ablation area is constructed to analyze the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs;

[0011] S4. Based on the analysis results of abnormal blood flow status and damage status of the ablation area during the ablation of varicose veins in the patient's lower limbs, a dynamic surgical risk quantification model is constructed to quantify the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs;

[0012] S5. Based on the dynamic risk quantification results of the surgery during the patient's lower limb varicose vein ablation, the parameters of the ablation device are dynamically adjusted.

[0013] In a preferred technical solution of the present invention, step S2 analyzes the abnormal blood flow state during the ablation of varicose veins in the patient's lower limbs, specifically including:

[0014] S21. extracting blood flow status data and vascular structure data of the ablation area during the patient's lower limb varicose vein ablation process;

[0015] S22. Construct a blood flow abnormality state analysis model, import the blood flow state data and vascular structure data of the ablation area into the blood flow abnormality state analysis model, analyze the blood flow abnormality state during the patient's lower limb varicose vein ablation process, and obtain the blood flow abnormality state analysis results during the patient's lower limb varicose vein ablation process.

[0016] In the preferred technical solution of the present invention, the process of constructing the abnormal blood flow state analysis model in step S22 specifically includes:

[0017] S221. Analyze the dynamic blood flow resistance during the ablation of varicose veins in the patient's lower limbs based on the blood flow state data and vascular structure data of the ablation area;

[0018] S222. Analyzing the degree of blood reverse flow energy dissipation during the ablation of varicose veins in the patient's lower limbs based on the blood flow status data in the ablation area;

[0019] The formula for calculating the degree of blood counterflow energy dissipation is:

[0020] ;

[0021] Where Nh is the degree of blood backflow energy dissipation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, t1 is the duration of ablation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Indicates the reverse flow pressure of the blood in the current ablation vessel segment at the tth moment during the ablation duration in the blood flow state data, Indicates the positive pressure of the blood in the current ablation vessel segment at the tth moment during the ablation duration in the blood flow state data;

[0022] S223. Analyzing the abnormal blood flow state during the varicose vein ablation of the patient's lower limbs based on the dynamic blood flow resistance state and the degree of blood reverse flow energy dissipation during the varicose vein ablation of the patient's lower limbs;

[0023] The calculation formula for abnormal blood flow status is:

[0024] ;

[0025] Where LF is the abnormal blood flow state of the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Xz is the dynamic blood flow resistance state of the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, and Nh is the degree of blood counterflow energy dissipation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs.

[0026] In a preferred technical solution of the present invention, step S3 analyzes the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs, which specifically includes the following steps:

[0027] S31, extracting temperature field data of the ablation region and blood vessel structure data;

[0028] S32. Construct an ablation area damage state analysis model, import the ablation area temperature field data and vascular structure data into the ablation area damage state analysis model, analyze the ablation area damage state during the patient's lower limb varicose vein ablation process, and obtain the ablation area damage state analysis results during the patient's lower limb varicose vein ablation process.

[0029] In the preferred technical solution of the present invention, the process of constructing the damage state analysis model of the ablation area in step S32 includes the following specific steps:

[0030] S321. Analyzing the thermal damage state of the vascular structure in the ablation area during the ablation of varicose veins in the patient's lower limbs based on the temperature field data and vascular structure data of the ablation area;

[0031] The calculation formula for the thermal damage status of vascular structures is:

[0032] ;

[0033] Where Rs is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs. is the vascular wall density of the current ablation vessel segment in the vascular structure data, Cp is the vascular wall specific heat capacity of the current ablation vessel segment in the ablation area temperature field data, is the ablation temperature of the current ablation vessel segment at the tth moment during the ablation duration in the ablation region temperature field data, To is the initial temperature of the vessel wall of the current ablation vessel segment in the ablation region temperature field data, is the rate of change of the average temperature of the vessel wall of the current ablated vessel segment in the ablation area temperature field data over time, k is the thermal conductivity of the vessel wall of the current ablated vessel segment in the ablation area temperature field data, Ah is the cross-sectional area of ​​the current ablated vessel segment in the vessel structure data, and d is the thickness of the vessel wall of the current ablated vessel segment in the vessel structure data;

[0034] S322. Analyzing the dynamic accumulation of thermal stress in the ablation area during the ablation of varicose veins in the patient's lower limbs based on the ablation area temperature field data and vascular structure data;

[0035] S323. Analyzing the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs based on the thermal damage state of the vascular structure and the dynamic accumulation degree of thermal stress in the ablation area during the ablation of varicose veins in the patient's lower limbs;

[0036] The calculation formula for the damage status of the ablation area is:

[0037] ;

[0038] Where, is the damage status of the ablation area of ​​the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Rs is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs, and Rd is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs.

[0039] In a preferred technical solution of the present invention, constructing a dynamic surgical risk quantification model in step S4 includes the following specific steps:

[0040] S41. Obtaining analysis results of abnormal blood flow status and damage status of the ablation area during ablation of varicose veins in the patient's lower extremities;

[0041] S42. Quantifying the dynamic risk of the surgery during the varicose vein ablation of the patient's lower limbs based on the analysis results of the abnormal blood flow status and the analysis results of the damage status of the ablation area during the varicose vein ablation of the patient's lower limbs, thereby obtaining a quantified result of the dynamic risk of the surgery during the varicose vein ablation of the patient's lower limbs;

[0042] The dynamic risk of surgery is calculated as follows:

[0043] ;

[0044] Where RT is the surgical dynamic risk of the current ablation vessel segment in the ablation area during the patient's lower limb varicose vein ablation process.

[0045] In a preferred technical solution of the present invention, the dynamic adjustment of the parameters of the ablation device in step S5 specifically includes:

[0046] S51. Obtaining the maximum value of the surgical dynamic risk quantification results of all completed ablated vascular segments in the ablation area during the patient's lower limb varicose vein ablation process as the ablation surgical dynamic risk threshold, and simultaneously obtaining the surgical dynamic risk quantification result of the current ablated vascular segment;

[0047] S52. When the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is greater than or equal to the dynamic risk threshold of the ablation surgery, reduce the microwave output power of the ablation device and shorten the microwave output time at the same time until the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is less than the dynamic risk threshold of the ablation surgery, and stop dynamically adjusting the parameters of the ablation device; when the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is less than the dynamic risk threshold of the ablation surgery, continue the ablation operation.

[0048] In a second aspect, the present invention provides an ablation process data management system based on big data evaluation, comprising:

[0049] The data acquisition module is used to obtain blood flow status data and vascular structure data of the ablation area during the ablation of varicose veins in the patient's lower limbs, and simultaneously obtain temperature field data of the ablation area;

[0050] The blood flow abnormality analysis module is used to build a blood flow abnormality analysis model based on the blood flow status data and vascular structure data of the ablation area, and analyze the blood flow abnormality during the ablation process of varicose veins in the patient's lower limbs;

[0051] The ablation area damage state analysis module is used to construct an ablation area damage state analysis model based on the ablation area temperature field data and vascular structure data, and analyze the damage state of the ablation area during the ablation process of varicose veins in the patient's lower limbs;

[0052] The surgical dynamic risk quantification module is used to construct a surgical dynamic risk quantification model based on the analysis results of abnormal blood flow status and damage status analysis results of the ablation area during the patient's lower limb varicose vein ablation process, and quantify the surgical dynamic risk during the patient's lower limb varicose vein ablation process;

[0053] The ablation device parameter dynamic adjustment module is used to dynamically adjust the ablation device parameters based on the dynamic risk quantification results of the surgery during the patient's lower limb varicose vein ablation process;

[0054] The control module is used to control the operation of the data acquisition module, the blood flow abnormality status analysis module, the ablation area damage status analysis module, the surgical dynamic risk quantification module, and the ablation device parameter dynamic adjustment module.

[0055] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an ablation process data management method based on big data evaluation by calling the computer program stored in the memory.

[0056] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0057] The present invention analyzes the abnormal blood flow state during the ablation of varicose veins in the patient's lower limbs through the blood flow status data and vascular structure data of the ablation area; analyzes the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs through the temperature field data and vascular structure data of the ablation area; quantifies the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs based on the analysis results of the abnormal blood flow state and the analysis results of the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs; and dynamically adjusts the parameters of the ablation device based on the quantified results of the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs; thereby improving the success rate of the operation, reducing the surgical risk, and improving the patient's postoperative quality of life. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0059] Figure 1 Schematic diagram of the overall process of the ablation process data management method based on big data evaluation of the present invention;

[0060] Figure 2 Schematic diagram of the structure of the ablation process data management system based on big data evaluation of the present invention;

[0061] Figure 3This is an analysis flow chart of step S2 of the ablation process data management method based on big data evaluation of the present invention;

[0062] Figure 4 This is an analysis flow chart of step S3 of the ablation process data management method based on big data evaluation of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment provides an ablation process data management method based on big data evaluation, which specifically includes the following steps:

[0066] S1. Obtain blood flow status data and vascular structure data in the ablation area during the ablation of varicose veins in the patient's lower limbs, and simultaneously obtain temperature field data in the ablation area;

[0067] S2. Based on the blood flow status data and vascular structure data of the ablation area, a blood flow abnormality analysis model is constructed to analyze the abnormal blood flow status during the ablation of varicose veins in the patient's lower limbs;

[0068] S3. Based on the temperature field data and vascular structure data of the ablation area, a damage state analysis model of the ablation area is constructed to analyze the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs;

[0069] S4. Based on the analysis results of abnormal blood flow status and damage status of the ablation area during the ablation of varicose veins in the patient's lower limbs, a dynamic surgical risk quantification model is constructed to quantify the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs;

[0070] S5. Based on the dynamic risk quantification results of the surgery during the patient's lower limb varicose vein ablation, the parameters of the ablation device are dynamically adjusted.

[0071] In this embodiment, if Figure 3 As shown, in step S2, the abnormal blood flow state during the ablation of varicose veins in the patient's lower limbs is analyzed, specifically including:

[0072] S21. extracting blood flow status data and vascular structure data of the ablation area during the patient's lower limb varicose vein ablation process;

[0073] S22. Construct a blood flow abnormality state analysis model, import the blood flow state data and vascular structure data of the ablation area into the blood flow abnormality state analysis model, analyze the blood flow abnormality state during the patient's lower limb varicose vein ablation process, and obtain the blood flow abnormality state analysis results during the patient's lower limb varicose vein ablation process.

[0074] In this embodiment, the process of constructing the abnormal blood flow state analysis model in step S22 specifically includes:

[0075] S221. Analyze the dynamic blood flow resistance during the ablation of varicose veins in the patient's lower limbs based on the blood flow state data and vascular structure data of the ablation area;

[0076] The calculation formula of dynamic blood flow resistance state is:

[0077] ;

[0078] Where Xz is the dynamic blood flow resistance state of the current ablation vessel segment during the patient's lower limb varicose vein ablation process, is the pressure difference between the two ends of the current ablation vessel segment in the blood flow state data, L is the length of the current ablation vessel segment in the vascular structure data, u is the blood viscosity in the current ablation vessel segment in the blood flow state data, R is the radius of the current ablation vessel segment in the vascular structure data, and Re is the Reynolds number of the blood flow in the current ablation vessel segment in the blood flow state data; Indicates taking all ablated vessel segments The minimum value of the calculation result;

[0079] Exemplarily, the calculation formula of the dynamic blood flow resistance state is used to quantify the change in blood flow resistance caused by structural changes in the blood vessel segment during the ablation process. In this embodiment, the ablation of varicose veins in the patient's lower limbs will cause the blood vessel wall to contract or harden, which directly affects the hemodynamic characteristics. This formula combines Poiseuille's law and the Reynolds number, which not only describes the relationship between the pressure difference and flow rate and blood vessel geometric parameters under the laminar flow state of blood, but also distinguishes between laminar and turbulent states. By introducing pressure difference, blood vessel length, blood viscosity, blood vessel radius and Reynolds number, the dynamic impact of vascular structural changes on blood flow resistance can be comprehensively reflected. Specifically, this embodiment evaluates the changes in blood flow resistance of the ablated blood vessel segment in real time to help identify abnormal resistance increases caused by the ablation operation; by introducing , thereby eliminating the influence of differences in lower extremity venous vessel size and enabling accurate comparison of the resistance status of different vascular segments.

[0080] S222. Analyzing the degree of blood reverse flow energy dissipation during the ablation of varicose veins in the patient's lower limbs based on the blood flow status data in the ablation area;

[0081] The formula for calculating the degree of blood counterflow energy dissipation is:

[0082] ;

[0083] Where Nh is the degree of blood backflow energy dissipation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, t1 is the duration of ablation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Indicates the reverse flow pressure of the blood in the current ablation vessel segment at the tth moment during the ablation duration in the blood flow state data, Indicates the positive pressure of the blood in the current ablation vessel segment at the tth moment during the ablation duration in the blood flow state data;

[0084] For example, patients with varicose veins of the lower limbs often suffer from valvular insufficiency, which leads to blood backflow. During the ablation process, thermal energy may further damage the valve or blood vessel wall, further exacerbating the backflow. The formula for calculating the degree of blood backflow energy dissipation in this embodiment quantifies the cumulative dissipation of backflow energy by integrating the ratio of the backflow pressure generated by blood flow on the blood vessel wall to the forward pressure generated by blood flow on the blood vessel wall over time, and can evaluate the impact of the ablation operation on the backflow energy. When the degree of blood backflow energy dissipation is too high, it indicates that the backflow energy is not effectively suppressed, which may lead to postoperative recurrence or increased risk of thrombosis. By real-time monitoring of the degree of blood backflow energy dissipation, this embodiment can dynamically adjust the ablation power or duration to optimize energy distribution and reduce blood backflow. Furthermore, this embodiment uses the local pressure ratio as a proxy variable for energy loss, which simplifies the complex three-dimensional integral calculation while retaining the physical correlation between the backflow energy and the pressure gradient. For example, during ablation, the heat energy will gradually diffuse to the surrounding blood vessel walls. This heat energy conduction process will lead to the gradual attenuation of the backflow energy. At the same time, under continuous heat exposure, the blood vessel walls may undergo physiological changes such as hardening or contraction, thereby further suppressing the backflow phenomenon. When the ablation procedure is completed, the local blood flow will be redistributed. This series of processes can reduce the pressure generated by the backflow. Therefore, by introducing ,This embodiment realizes the simulation analysis of the natural dissipation process of countercurrent energy over time.

[0085] S223. Analyzing the abnormal blood flow state during the varicose vein ablation of the patient's lower limbs based on the dynamic blood flow resistance state and the degree of blood reverse flow energy dissipation during the varicose vein ablation of the patient's lower limbs;

[0086] The calculation formula for abnormal blood flow status is:

[0087] ;

[0088] Where LF is the abnormal blood flow state of the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Xz is the dynamic blood flow resistance state of the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, and Nh is the degree of blood counterflow energy dissipation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs.

[0089] For example, this embodiment comprehensively evaluates the abnormal state of hemodynamics in the vascular segment during the ablation process, focusing on the synergistic effect of dynamic blood flow resistance and the degree of blood reverse flow energy dissipation. Varicose vein ablation may cause vascular wall contraction, hardening or valvular insufficiency. These structural changes will significantly increase blood flow resistance and aggravate blood reverse flow. This embodiment introduces a logarithmic function term to smooth the nonlinear effect of reverse flow energy dissipation to avoid distortion of calculation results caused by instantaneous surges in reverse flow energy, such as short-term high pressure differences. At the same time, since the effect of reverse flow energy dissipation on blood flow abnormalities is small in the low value range, but as the dissipated energy increases, its contribution to the overall risk increases exponentially, therefore, this embodiment uses a logarithmic function to effectively fit this upward trend.

[0090] In this embodiment, if Figure 4 As shown, in step S3, the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs is analyzed, which specifically includes the following steps:

[0091] S31, extracting temperature field data of the ablation region and blood vessel structure data;

[0092] S32. Construct an ablation area damage state analysis model, import the ablation area temperature field data and vascular structure data into the ablation area damage state analysis model, analyze the ablation area damage state during the patient's lower limb varicose vein ablation process, and obtain the ablation area damage state analysis results during the patient's lower limb varicose vein ablation process.

[0093] In this embodiment, the process of constructing the damage state analysis model of the ablation area in step S32 includes the following specific steps:

[0094] S321. Analyzing the thermal damage state of the vascular structure in the ablation area during the ablation of varicose veins in the patient's lower limbs based on the temperature field data and vascular structure data of the ablation area;

[0095] The calculation formula for the thermal damage status of vascular structures is:

[0096] ;

[0097] Where Rs is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs. is the vascular wall density of the current ablation vessel segment in the vascular structure data, Cp is the vascular wall specific heat capacity of the current ablation vessel segment in the ablation area temperature field data, is the ablation temperature of the current ablation vessel segment at the tth moment during the ablation duration in the ablation region temperature field data, To is the initial temperature of the vessel wall of the current ablation vessel segment in the ablation region temperature field data, is the rate of change of the average temperature of the vessel wall of the current ablated vessel segment in the ablation area temperature field data over time, k is the thermal conductivity of the vessel wall of the current ablated vessel segment in the ablation area temperature field data, Ah is the cross-sectional area of ​​the current ablated vessel segment in the vessel structure data, and d is the thickness of the vessel wall of the current ablated vessel segment in the vessel structure data;

[0098] Exemplarily, this embodiment is used to quantify the degree of cumulative structural damage to the blood vessel wall caused by thermal energy during varicose vein ablation. Ablation surgery uses thermal energy to denature blood vessel wall proteins and shrink collagen fibers, thereby achieving blood vessel closure. However, excessive heat exposure may lead to vascular perforation or irreversible scarring. Therefore, this embodiment can capture the accumulation of thermal stress throughout the entire ablation cycle in an integral form. Specifically, the density and specific heat capacity of the blood vessel wall determine the ability of unit volume tissue to absorb thermal energy; thermal conductivity can reflect the efficiency of heat diffusion along the blood vessel wall; cross-sectional area and wall thickness characterize the regulatory effect of blood vessel geometry on heat distribution. By introducing the above parameters, it can be ensured that the calculation formula for the thermal damage state of the blood vessel structure in this embodiment is applicable to blood vessel segments of different sizes. Specifically, the product term of the temperature difference and the temperature change rate can simulate the synergistic amplification effect of the heating rate and temperature difference on blood vessel damage during the ablation process. For example, rapid heating makes Increased temperature may cause instantaneous thermal shock under high temperature difference, aggravating vascular tissue damage.

[0099] S322. Analyzing the dynamic accumulation of thermal stress in the ablation area during the ablation of varicose veins in the patient's lower limbs based on the ablation area temperature field data and vascular structure data;

[0100] The calculation formula for the dynamic accumulation degree of thermal stress is:

[0101] ;

[0102] Where Rd is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs, a is the thermal expansion coefficient of the vessel wall of the current ablation vessel segment in the vascular structure data, E is the elastic modulus of the current ablation vessel segment in the vascular structure data, and Qx is the yield strength of the vessel wall of the current ablation vessel segment in the vascular structure data. is the vascular wall temperature gradient of the current ablated vessel segment at the tth moment during the ablation duration in the temperature field data of the ablation area;

[0103] Illustratively, this embodiment is used to quantify the dynamic cumulative effect of thermal stress on the blood vessel wall caused by the temperature gradient during the ablation process. When microwave ablation is performed on varicose veins in the patient's lower limbs, thermal energy is transferred to the blood vessel wall through the catheter, causing the local temperature gradient to increase, thereby inducing thermal expansion and mechanical stress. The accumulation of thermal stress is closely related to the geometric structure of the blood vessel and the temperature field. The calculation formula for the dynamic accumulation of thermal stress in this embodiment captures the stress accumulation during the entire ablation cycle in an integral form, and introduces the denominator as a normalization factor to eliminate the influence of individual differences in blood vessels, ensuring that this embodiment can be applied to the vascular characteristics of different patients. Specifically, the dynamic accumulation of thermal stress in this embodiment reflects the risk of fatigue damage to the blood vessel wall due to thermal expansion and contraction cycles. When the dynamic accumulation of thermal stress increases sharply, it may be necessary to adjust the ablation mode of the ablation device to reduce the risk of cracks or loss of elasticity in the blood vessel wall.

[0104] S323. Analyzing the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs based on the thermal damage state of the vascular structure and the dynamic accumulation degree of thermal stress in the ablation area during the ablation of varicose veins in the patient's lower limbs;

[0105] The calculation formula for the damage status of the ablation area is:

[0106] ;

[0107] Where, is the damage status of the ablation area of ​​the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Rs is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs, and Rd is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs.

[0108] Illustratively, this embodiment comprehensively evaluates the vascular damage state caused by the synergistic effect of thermal stress and mechanical stress on the vascular segment during the ablation process. When ablation is performed on varicose veins in the patient's lower limbs, thermal energy not only directly induces tissue degeneration, but also generates thermal expansion stress due to the temperature gradient. The two together determine the final degree of vascular damage. The calculation formula for the damage state of the ablation area in this embodiment effectively suppresses the interference of abnormal values ​​that may be generated after normalization due to the synergistic effect of the two stress mechanisms through the hyperbolic tangent function, so that the damage state is limited to a physically explainable range, avoiding misjudgment due to calculation overflow; at the same time, this embodiment also emphasizes the nonlinear superposition of thermal stress and mechanical stress through the product of the thermal damage state of the vascular structure and the dynamic accumulation degree of thermal stress. For example, high thermal stress may soften the vascular wall, thereby amplifying the destructive effect of mechanical stress.

[0109] In this embodiment, the construction of the dynamic surgical risk quantification model in step S4 includes the following specific steps:

[0110] S41. Obtaining analysis results of abnormal blood flow status and damage status of the ablation area during ablation of varicose veins in the patient's lower extremities;

[0111] S42. Quantifying the dynamic risk of the surgery during the varicose vein ablation of the patient's lower limbs based on the analysis results of the abnormal blood flow status and the analysis results of the damage status of the ablation area during the varicose vein ablation of the patient's lower limbs, thereby obtaining a quantified result of the dynamic risk of the surgery during the varicose vein ablation of the patient's lower limbs;

[0112] The dynamic risk of surgery is calculated as follows:

[0113] ;

[0114] Where RT is the surgical dynamic risk of the current ablation vessel segment in the ablation area during the patient's lower limb varicose vein ablation process.

[0115] Exemplarily, this embodiment is used to dynamically quantify the surgical risk under the synergistic effect of blood flow abnormalities and tissue damage during varicose vein ablation. In varicose vein ablation surgery of the lower limbs, the risk of surgery depends not only on the influence of single factors and independent effects of blood flow abnormalities or thermal damage, but also on the nonlinear superposition of the two. This embodiment captures the synergistic effect of the two through the geometric mean term. Specifically, in the calculation formula of the dynamic risk of surgery, the numerator uses the hyperbolic tangent function to compress the geometric mean term into a numerical range of plus or minus one to avoid overflow of the calculation result caused by extreme values; the denominator uses , and then map the two to the numerical range of 0-1 to ensure that the final dynamic risk of the operation falls within a reasonable range. Furthermore, during the ablation of thin-walled blood vessels, this embodiment can dynamically couple the abnormal blood flow state with the damage state of the ablation area, and can accurately analyze the situation where the high blood flow abnormality may aggravate the temperature gradient through the change of flow rate, thereby affecting the damage state of the ablation area. Furthermore, this embodiment uses the hyperbolic tangent function to simulate the "saturation response" of the biological system to damage, such as the situation where the blood vessel wall no longer deteriorates significantly after reaching critical damage, so that the technical solution provided by this embodiment not only conforms to the nonlinear response characteristics of the biological system, but also provides an operational risk indicator for clinical real-time decision-making, and realizes the accurate quantification of surgical risks.

[0116] In this embodiment, the dynamic adjustment of the parameters of the ablation device in step S5 specifically includes:

[0117] S51. Obtaining the maximum value of the surgical dynamic risk quantification results of all completed ablated vascular segments in the ablation area during the patient's lower limb varicose vein ablation process as the ablation surgical dynamic risk threshold, and simultaneously obtaining the surgical dynamic risk quantification result of the current ablated vascular segment;

[0118] S52. When the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is greater than or equal to the dynamic risk threshold of the ablation surgery, reduce the microwave output power of the ablation device and shorten the microwave output time at the same time until the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is less than the dynamic risk threshold of the ablation surgery, and stop dynamically adjusting the parameters of the ablation device; when the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is less than the dynamic risk threshold of the ablation surgery, continue the ablation operation.

[0119] Example 2

[0120] like Figure 2 As shown, this embodiment provides an ablation process data management system based on big data evaluation, including:

[0121] The data acquisition module is used to obtain blood flow status data and vascular structure data of the ablation area during the ablation of varicose veins in the patient's lower limbs, and simultaneously obtain temperature field data of the ablation area;

[0122] The blood flow abnormality analysis module is used to build a blood flow abnormality analysis model based on the blood flow status data and vascular structure data of the ablation area, and analyze the blood flow abnormality during the ablation process of varicose veins in the patient's lower limbs;

[0123] The ablation area damage state analysis module is used to construct an ablation area damage state analysis model based on the ablation area temperature field data and vascular structure data, and analyze the damage state of the ablation area during the ablation process of varicose veins in the patient's lower limbs;

[0124] The surgical dynamic risk quantification module is used to construct a surgical dynamic risk quantification model based on the analysis results of abnormal blood flow status and damage status analysis results of the ablation area during the patient's lower limb varicose vein ablation process, and quantify the surgical dynamic risk during the patient's lower limb varicose vein ablation process;

[0125] The ablation device parameter dynamic adjustment module is used to dynamically adjust the ablation device parameters based on the dynamic risk quantification results of the surgery during the patient's lower limb varicose vein ablation process;

[0126] The control module is used to control the operation of the data acquisition module, the blood flow abnormality status analysis module, the ablation area damage status analysis module, the surgical dynamic risk quantification module, and the ablation device parameter dynamic adjustment module.

[0127] The above-mentioned parameters and steps for each unit module to realize corresponding functions in the ablation process data management system based on big data evaluation of the present invention can refer to the parameters and steps in the embodiment of the ablation process data management method based on big data evaluation above, and will not be repeated here.

[0128] Example 3

[0129] An electronic device according to an embodiment of the present invention includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an ablation process data management method based on big data evaluation by calling the computer program stored in the memory. It should be noted that all computer programs of the ablation process data management method based on big data evaluation are implemented using the C language, wherein the data acquisition module, the blood flow abnormality status analysis module, the ablation area damage status analysis module, the surgical dynamic risk quantification module, the ablation device parameter dynamic adjustment module, and the control module are all controlled by a remote server.

[0130] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0131] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The ablation process data management method based on big data evaluation is characterized by: The steps include: S1. Obtain blood flow status data and vascular structure data in the ablation area during the ablation of varicose veins in the patient's lower limbs, and simultaneously obtain temperature field data in the ablation area; S2. Based on the blood flow status data and vascular structure data of the ablation area, a blood flow abnormality analysis model is constructed to analyze the abnormal blood flow status during the ablation of varicose veins in the patient's lower limbs; S3. Based on the temperature field data and vascular structure data of the ablation area, a damage state analysis model of the ablation area is constructed to analyze the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs; S4. Based on the analysis results of abnormal blood flow status and damage status of the ablation area during the ablation of varicose veins in the patient's lower limbs, a dynamic surgical risk quantification model is constructed to quantify the dynamic surgical risk during the ablation of varicose veins in the patient's lower limbs; S5. Dynamically adjust the parameters of the ablation device based on the dynamic risk quantification results of the surgery during the patient's lower limb varicose vein ablation process; The step S2 analyzes the abnormal blood flow state during the ablation of varicose veins in the patient's lower limbs, specifically including: S21. extracting blood flow status data and vascular structure data of the ablation area during the patient's lower limb varicose vein ablation process; S22. Constructing a blood flow abnormality state analysis model, importing the blood flow state data and vascular structure data of the ablation area into the blood flow abnormality state analysis model, analyzing the blood flow abnormality state during the ablation of varicose veins in the patient's lower limbs, and obtaining the blood flow abnormality state analysis results during the ablation of varicose veins in the patient's lower limbs; The process of constructing the abnormal blood flow state analysis model in step S22 specifically includes: S221. Analyze the dynamic blood flow resistance during the ablation of varicose veins in the patient's lower limbs based on the blood flow state data and vascular structure data of the ablation area; The calculation formula of dynamic blood flow resistance state is: ； Where Xz is the dynamic blood flow resistance state of the current ablation vessel segment during the patient's lower limb varicose vein ablation process, is the pressure difference between the two ends of the current ablation vessel segment in the blood flow state data, L is the length of the current ablation vessel segment in the vascular structure data, u is the blood viscosity in the current ablation vessel segment in the blood flow state data, R is the radius of the current ablation vessel segment in the vascular structure data, and Re is the Reynolds number of the blood flow in the current ablation vessel segment in the blood flow state data; Indicates taking all ablated vessel segments The minimum value of the calculation result; S222. Analyzing the degree of blood reverse flow energy dissipation during the ablation of varicose veins in the patient's lower limbs based on the blood flow status data in the ablation area; The formula for calculating the degree of blood counterflow energy dissipation is: ； Where Nh is the degree of blood backflow energy dissipation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, t1 is the duration of ablation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Indicates the reverse flow pressure of the blood in the current ablation vessel segment at the tth moment during the ablation duration in the blood flow state data, Indicates the positive pressure of the blood in the current ablation vessel segment at the tth moment during the ablation duration in the blood flow state data; S223. Analyzing the abnormal blood flow state during the varicose vein ablation of the patient's lower limbs based on the dynamic blood flow resistance state and the degree of blood reverse flow energy dissipation during the varicose vein ablation of the patient's lower limbs; The calculation formula for abnormal blood flow status is: ； Where LF is the abnormal blood flow state of the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Xz is the dynamic blood flow resistance state of the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, and Nh is the degree of blood counterflow energy dissipation in the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs.

2. The ablation process data management method based on big data evaluation according to claim 1, characterized in that: The step S3 analyzes the damage status of the ablation area during the ablation of varicose veins in the patient's lower limbs, specifically including the following steps: S31, extracting temperature field data of the ablation region and blood vessel structure data; S32. Construct an ablation area damage state analysis model, import the ablation area temperature field data and vascular structure data into the ablation area damage state analysis model, analyze the ablation area damage state during the patient's lower limb varicose vein ablation process, and obtain the ablation area damage state analysis results during the patient's lower limb varicose vein ablation process.

3. The ablation process data management method based on big data evaluation according to claim 2, characterized in that: The process of constructing the damage state analysis model of the ablation area in step S32 includes the following specific steps: S321. Analyzing the thermal damage state of the vascular structure in the ablation area during the ablation of varicose veins in the patient's lower limbs based on the temperature field data and vascular structure data of the ablation area; The calculation formula for the thermal damage status of vascular structures is: ； Where Rs is the thermal damage state of the vascular structure of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs, is the vascular wall density of the current ablation vessel segment in the vascular structure data, Cp is the vascular wall specific heat capacity of the current ablation vessel segment in the ablation area temperature field data, is the ablation temperature of the current ablation vessel segment at the tth moment during the ablation duration in the ablation region temperature field data, To is the initial temperature of the vessel wall of the current ablation vessel segment in the ablation region temperature field data, is the rate of change of the average temperature of the vessel wall of the current ablated vessel segment in the ablation area temperature field data over time, k is the thermal conductivity of the vessel wall of the current ablated vessel segment in the ablation area temperature field data, Ah is the cross-sectional area of ​​the current ablated vessel segment in the vessel structure data, and d is the thickness of the vessel wall of the current ablated vessel segment in the vessel structure data; S322. Analyzing the dynamic accumulation of thermal stress in the ablation area during the ablation of varicose veins in the patient's lower limbs based on the ablation area temperature field data and vascular structure data; The calculation formula for the dynamic accumulation degree of thermal stress is: ； Where Rd is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs, a is the thermal expansion coefficient of the vessel wall of the current ablation vessel segment in the vascular structure data, E is the elastic modulus of the current ablation vessel segment in the vascular structure data, and Qx is the yield strength of the vessel wall of the current ablation vessel segment in the vascular structure data. is the vascular wall temperature gradient of the current ablated vessel segment at the tth moment during the ablation duration in the temperature field data of the ablation area; S323. Analyzing the damage state of the ablation area during the ablation of varicose veins in the patient's lower limbs based on the thermal damage state of the vascular structure and the dynamic accumulation degree of thermal stress in the ablation area during the ablation of varicose veins in the patient's lower limbs; The calculation formula for the damage status of the ablation area is: ； Where XS is the damage status of the ablation area of ​​the current ablation vessel segment during the ablation of varicose veins in the patient's lower limbs, Rs is the thermal damage status of the vascular structure of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs, and Rd is the dynamic accumulation degree of thermal stress of the current ablation vessel segment in the ablation area during the ablation of varicose veins in the patient's lower limbs.

4. The ablation process data management method based on big data evaluation according to claim 3, characterized in that: The step S4 constructs a dynamic surgical risk quantification model, including the following specific steps: S41. Obtaining analysis results of abnormal blood flow status and damage status of the ablation area during ablation of varicose veins in the patient's lower extremities; S42. Quantifying the dynamic risk of the surgery during the varicose vein ablation of the patient's lower limbs based on the analysis results of the abnormal blood flow status and the analysis results of the damage status of the ablation area during the varicose vein ablation of the patient's lower limbs, thereby obtaining a quantified result of the dynamic risk of the surgery during the varicose vein ablation of the patient's lower limbs; The dynamic risk of surgery is calculated as follows: ； Where RT is the surgical dynamic risk of the current ablation vessel segment in the ablation area during the patient's lower limb varicose vein ablation process.

5. The ablation process data management method based on big data evaluation according to claim 4, characterized in that: The step S5 of dynamically adjusting the parameters of the ablation device specifically includes: S51. Obtaining the maximum value of the surgical dynamic risk quantification results of all completed ablated vascular segments in the ablation area during the patient's lower limb varicose vein ablation process as the ablation surgical dynamic risk threshold, and simultaneously obtaining the surgical dynamic risk quantification result of the current ablated vascular segment; S52. When the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is greater than or equal to the dynamic risk threshold of the ablation surgery, reduce the microwave output power of the ablation device and shorten the microwave output time at the same time until the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is less than the dynamic risk threshold of the ablation surgery, and stop dynamically adjusting the parameters of the ablation device; when the quantification result of the dynamic surgical risk of the current ablation blood vessel segment is less than the dynamic risk threshold of the ablation surgery, continue the ablation operation.

6. An ablation process data management system based on big data evaluation, used to implement the ablation process data management method based on big data evaluation according to any one of claims 1 to 5, characterized in that: The system comprises: The data acquisition module is used to obtain blood flow status data and vascular structure data of the ablation area during the ablation of varicose veins in the patient's lower limbs, and simultaneously obtain temperature field data of the ablation area; The blood flow abnormality analysis module is used to build a blood flow abnormality analysis model based on the blood flow status data and vascular structure data of the ablation area, and analyze the blood flow abnormality during the ablation process of varicose veins in the patient's lower limbs; The ablation area damage state analysis module is used to construct an ablation area damage state analysis model based on the ablation area temperature field data and vascular structure data, and analyze the damage state of the ablation area during the ablation process of varicose veins in the patient's lower limbs; The surgical dynamic risk quantification module is used to construct a surgical dynamic risk quantification model based on the analysis results of abnormal blood flow status and damage status analysis results of the ablation area during the patient's lower limb varicose vein ablation process, and quantify the surgical dynamic risk during the patient's lower limb varicose vein ablation process; The ablation device parameter dynamic adjustment module is used to dynamically adjust the ablation device parameters based on the dynamic risk quantification results of the surgery during the patient's lower limb varicose vein ablation process; A control module is used to control the operation of the data acquisition module, the blood flow abnormality state analysis module, the ablation area damage state analysis module, the surgical dynamic risk quantification module, and the ablation device parameter dynamic adjustment module.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the ablation process data management method based on big data evaluation as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

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