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

Abstract

The invention relates to the technical field of monitoring data analysis, in particular to an ablation process data management system and method based on big data assessment, and the method comprises the steps: analyzing the abnormal blood flow state in the lower limb varicose vein ablation process of a patient through the blood flow state data and vascular structure data of an ablation region; analyzing the injury state of the ablation area in the lower limb varicose vein ablation process of the patient according to the temperature field data and the vascular structure data of the ablation area; according to a blood flow abnormal state analysis result and an ablation area damage state analysis result in the lower limb varicose vein ablation process of the patient, quantifying the operation dynamic risk in the lower limb varicose vein ablation process of the patient; dynamically adjusting parameters of ablation equipment based on the operation dynamic risk quantification result in the lower limb varicose vein ablation process of the patient; therefore, the success rate of an operation is improved, the operation risk is reduced, and the postoperative life quality of a patient is improved.

Description

Technical Field

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

[0002] Varicose veins of the lower extremities is a common vascular disease, and its physiological characteristics are manifested as abnormal dilation and tortuosity of the lower extremity venous vessels. In daily life, it will not only seriously affect the appearance, but may also cause a series of symptoms such as pain, swelling, and even skin ulcers in patients, greatly reducing the quality of life of patients. Traditional treatments for varicose veins of the lower extremities, such as drug treatment, pressure treatment, and surgical operations, although having certain effects, are often accompanied by problems such as large trauma, slow recovery, and high recurrence rate. With the rapid development of medical technology, minimally invasive treatment has gradually become a new direction for the treatment of varicose veins of the lower extremities. Among them, microwave ablation technology has attracted much attention due to its unique advantages. Microwave ablation heats the diseased blood vessels by using microwave energy, promotes the denaturation and coagulation of the blood vessel wall proteins, and finally realizes the occlusion of the blood vessels.

[0003] However, the existing technologies often focus on microwave ablation related to solid tumors in other general parts, which have significant differences from varicose veins of the lower extremities in terms of anatomical structure, physiological function, and hemodynamic characteristics. Due to the failure to specifically optimize for this specific vascular disease of varicose veins of the lower extremities, when the existing technologies are applied to the treatment of varicose veins of the lower extremities, the essential differences between ablation of solid tumors in general parts and blood vessel ablation are not fully considered, lacking comprehensive monitoring and dynamic adjustment of the blood flow state, blood vessel structure, device parameters, and temperature field data during the ablation process. This results in incomplete ablation or over-ablation, increasing the surgical risk and affecting the final ablation effect.

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

[0005] The purpose of the present invention is to provide an ablation process data management system and method based on big data evaluation, which constructs an analysis model for abnormal blood flow states, an analysis model for damage states of ablation regions, and a quantification model for dynamic surgical risks to conduct real-time evaluation and quantification of various risks during the surgical process. According to the quantification results of dynamic surgical risks, this solution can dynamically adjust the parameters of the ablation device, such as ablation temperature and ablation speed, to ensure the safety and effectiveness of the surgery. At the same time, it not only improves the success rate of the surgery, reduces the surgical risk, but also improves the postoperative quality of life of patients.

[0006] The present invention is implemented as follows: In the first aspect, the present invention provides an ablation process data management method based on big data evaluation, including the following steps: S1. Obtain the blood flow state data and vascular structure data of the ablation area during the ablation of varicose veins in the lower extremities of the patient, and at the same time obtain the temperature field data of the ablation area; S2. Based on the blood flow state data and vascular structure data of the ablation area, construct an analysis model for abnormal blood flow states, and analyze the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient; S3. Based on the temperature field data and vascular structure data of the ablation area, construct an analysis model for the damage state of the ablation area, and analyze the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient; S4. According to the analysis results of the abnormal blood flow states and the analysis results of the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient, construct a dynamic surgical risk quantification model to quantify the dynamic surgical risks during the ablation of varicose veins in the lower extremities of the patient; S5. Based on the quantification results of the dynamic surgical risks during the ablation of varicose veins in the lower extremities of the patient, dynamically adjust the parameters of the ablation device.

[0007] In the preferred technical solution of the present invention, the analysis of the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient in step S2 specifically includes: S21. Extract the blood flow state data and vascular structure data of the ablation area during the ablation of varicose veins in the lower extremities of the patient; S22. Construct an analysis model for abnormal blood flow states, import the blood flow state data and vascular structure data of the ablation area into the analysis model for abnormal blood flow states, analyze the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient, and obtain the analysis results of the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient.

[0008] In the preferred technical solution of the present invention, the construction process of the analysis model for abnormal blood flow states in step S22 specifically includes: S221. Based on the blood flow state data and vascular structure data of the ablation area, analyze the dynamic blood flow resistance state during the ablation of varicose veins in the lower extremities of the patient; S222. Based on the blood flow state data of the ablation area, analyze the degree of energy dissipation due to blood backflow during the ablation of varicose veins in the lower extremities of the patient; The formula for calculating the degree of energy dissipation due to blood backflow is: ; In the formula, Nh is the degree of energy dissipation due to blood backflow of the current ablation blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, t1 is the ablation duration of the current ablation blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, represents the backflow pressure of the blood at the t-th moment during the ablation duration of the current ablation blood vessel segment in the blood flow state data, It represents the positive pressure of blood at the t-th moment during the ablation duration of the current ablated blood vessel segment in the blood flow state data. S223. Analyze the abnormal blood flow state during the ablation of varicose veins in the lower extremities of the patient according to the dynamic blood flow resistance state and the degree of blood reverse energy dissipation during the ablation of varicose veins in the lower extremities of the patient. The calculation formula for the abnormal blood flow state is: ; In the formula, LF is the abnormal blood flow state of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, Xz is the dynamic blood flow resistance state of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, and Nh is the degree of blood reverse energy dissipation of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient.

[0009] In the preferred technical solution of the present invention, in step S3, analyze the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient, which specifically includes the following steps: S31. Extract the temperature field data and blood vessel structure data of the ablation area. S32. Construct an analysis model for the damage state of the ablation area, import the temperature field data and blood vessel structure data of the ablation area into the analysis model for the damage state of the ablation area, analyze the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient, and obtain the analysis result of the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient.

[0010] In the preferred technical solution of the present invention, the construction process of the analysis model for the damage state of the ablation area in step S32 includes the following specific steps: S321. Based on the temperature field data and blood vessel structure data of the ablation area, analyze the thermal damage state of the blood vessel structure in the ablation area during the ablation of varicose veins in the lower extremities of the patient. The calculation formula for the thermal damage state of the blood vessel structure is: ; In the formula, Rs is the degree of dynamic cumulative thermal stress of the current ablated blood vessel segment in the ablation area during the ablation of varicose veins in the lower extremities of the patient, is the blood vessel wall density of the current ablated blood vessel segment in the blood vessel structure data, Cp is the specific heat capacity of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation area, is the ablation temperature at the t-th moment during the ablation duration of the current ablated blood vessel segment in the temperature field data of the ablation area, and To is the initial temperature of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation area. is the rate of change of the average temperature of the blood vessel wall of the current ablated blood vessel segment with respect to time in the temperature field data of the ablation region, k is the thermal conductivity of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation region, Ah is the cross-sectional area of the current ablated blood vessel segment in the blood vessel structure data, and d is the thickness of the blood vessel wall of the current ablated blood vessel segment in the blood vessel structure data; S322. Analyze the dynamic cumulative degree of thermal stress in the ablation region during the ablation of varicose veins in the lower extremities of the patient based on the temperature field data of the ablation region and the blood vessel structure data; S323. Analyze the damage state of the ablation region during the ablation of varicose veins in the lower extremities of the patient according to the thermal damage state of the blood vessel structure and the dynamic cumulative degree of thermal stress in the ablation region during the ablation of varicose veins in the lower extremities of the patient; The calculation formula for the damage state of the ablation region is: ; In the formula, is the damage state of the ablation region of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, Rs is the dynamic cumulative degree of thermal stress in the ablation region of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, and Rd is the dynamic cumulative degree of thermal stress in the ablation region of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient.

[0011] In a preferred technical solution of the present invention, in step S4, constructing a surgical dynamic risk quantification model includes the following specific steps: S41. Obtain the analysis results of the abnormal blood flow state and the damage state analysis results of the ablation region during the ablation of varicose veins in the lower extremities of the patient; S42. Quantify the surgical dynamic risk during the ablation of varicose veins in the lower extremities of the patient according to the analysis results of the abnormal blood flow state and the damage state analysis results of the ablation region during the ablation of varicose veins in the lower extremities of the patient, and obtain the surgical dynamic risk quantification result during the ablation of varicose veins in the lower extremities of the patient; The calculation formula for the surgical dynamic risk is: ; In the formula, RT is the surgical dynamic risk of the current ablated blood vessel segment in the ablation region during the ablation of varicose veins in the lower extremities of the patient.

[0012] In a preferred technical solution of the present invention, in step S5, dynamically adjusting the parameters of the ablation device specifically includes: S51. Obtain the maximum value of the surgical dynamic risk quantification results of all the ablated blood vessel segments in the ablation region during the ablation of varicose veins in the lower extremities of the patient as the ablation surgical dynamic risk threshold, and at the same time obtain the surgical dynamic risk quantification result of the current ablated blood vessel segment; S52. When the quantified result of the surgical dynamic risk of the current ablated blood vessel segment is greater than or equal to the ablation surgery dynamic risk threshold, reduce the microwave output power of the ablation device and simultaneously shorten the microwave output time until the quantified result of the surgical dynamic risk of the current ablated blood vessel segment is less than the ablation surgery dynamic risk threshold, and then stop dynamically adjusting the parameters of the ablation device; when the quantified result of the surgical dynamic risk of the current ablated blood vessel segment is less than the ablation surgery dynamic risk threshold, continue the ablation operation.

[0013] In a second aspect, the present invention provides an ablation process data management system based on big data evaluation, including: A data acquisition module, configured to acquire blood flow state data and blood vessel structure data of the ablation area during the ablation of varicose veins in the lower extremities of a patient, and simultaneously acquire temperature field data of the ablation area; A blood flow abnormal state analysis module, configured to construct a blood flow abnormal state analysis model based on the blood flow state data and blood vessel structure data of the ablation area, and analyze the blood flow abnormal state during the ablation of varicose veins in the lower extremities of a patient; An ablation area damage state analysis module, configured to construct an ablation area damage state analysis model based on the temperature field data and blood vessel structure data of the ablation area, and analyze the ablation area damage state during the ablation of varicose veins in the lower extremities of a patient; A surgical dynamic risk quantification module, configured to construct a surgical dynamic risk quantification model according to the analysis results of the blood flow abnormal state and the ablation area damage state during the ablation of varicose veins in the lower extremities of a patient, and quantify the surgical dynamic risk during the ablation of varicose veins in the lower extremities of a patient; An ablation device parameter dynamic adjustment module, configured to dynamically adjust the parameters of the ablation device based on the quantified result of the surgical dynamic risk during the ablation of varicose veins in the lower extremities of a patient; A control module, configured to control the operation of the data acquisition module, the blood flow abnormal state analysis module, the ablation area damage state analysis module, the surgical dynamic risk quantification module, and the ablation device parameter dynamic adjustment module.

[0014] In a third aspect, the present invention provides an electronic device, including: a processor and a memory, wherein a computer program that can be called by the processor is stored in the memory, and the processor executes the ablation process data management method based on big data evaluation by calling the computer program stored in the memory.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention analyzes the abnormal blood flow state during the ablation of varicose veins in patients' lower extremities through the blood flow state data and vascular structure data of the ablation area; analyzes the damage state of the ablation area during the ablation of varicose veins in patients through the temperature field data and vascular structure data of the ablation area; quantifies the surgical dynamic risk during the ablation of varicose veins in patients according to the analysis results of the abnormal blood flow state and the damage state of the ablation area during the ablation of varicose veins in patients; dynamically adjusts the parameters of the ablation device based on the quantification results of the surgical dynamic risk during the ablation of varicose veins in patients; thereby improving the success rate of the surgery, reducing the surgical risk, and improving the postoperative quality of life of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 It is a schematic diagram of the overall process of the ablation process data management method based on big data evaluation of the present invention; Figure 2 It is a schematic diagram of the structure of the ablation process data management system based on big data evaluation of the present invention; Figure 3 It is an analysis flow chart of step S2 of the ablation process data management method based on big data evaluation of the present invention; Figure 4 It 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 OF THE EMBODIMENTS

[0017] The technical solution of the present invention will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present invention are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0018] Embodiment 1

[0019] As Figure 1 shown, this embodiment provides an ablation process data management method based on big data evaluation, which specifically includes the following steps: S1. Obtain the blood flow state data and vascular structure data of the ablation area during the ablation of varicose veins in patients, and at the same time obtain the temperature field data of the ablation area; S2. Based on the blood flow state data and vascular structure data of the ablation area, construct an analysis model for abnormal blood flow state, and analyze the abnormal blood flow state during the ablation of varicose veins in patients; S3. Based on the temperature field data and vascular structure data of the ablation area, construct an analysis model for the damage state of the ablation area to analyze the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient; S4. According to 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 lower extremities of the patient, construct a surgical dynamic risk quantification model to quantify the surgical dynamic risk during the ablation of varicose veins in the lower extremities of the patient; S5. Based on the quantification results of the surgical dynamic risk during the ablation of varicose veins in the lower extremities of the patient, dynamically adjust the parameters of the ablation device.

[0020] In this embodiment, as Figure 3 shown, the analysis of the abnormal blood flow state during the ablation of varicose veins in the lower extremities of the patient in step S2 specifically includes: S21. Extract the blood flow state data and vascular structure data of the ablation area during the ablation of varicose veins in the lower extremities of the patient; S22. Construct an analysis model for the abnormal blood flow state, import the blood flow state data and vascular structure data of the ablation area into the analysis model for the abnormal blood flow state, analyze the abnormal blood flow state during the ablation of varicose veins in the lower extremities of the patient, and obtain the analysis results of the abnormal blood flow state during the ablation of varicose veins in the lower extremities of the patient.

[0021] In this embodiment, the construction process of the analysis model for the abnormal blood flow state in step S22 specifically includes: S221. Based on the blood flow state data and vascular structure data of the ablation area, analyze the dynamic blood flow resistance state during the ablation of varicose veins in the lower extremities of the patient; The calculation formula for the dynamic blood flow resistance state is: ; In the formula, Xz is the dynamic blood flow resistance state of the current ablation blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, is the pressure difference at both ends of the current ablation blood vessel segment in the blood flow state data, L is the length of the current ablation blood vessel segment in the vascular structure data, u is the blood viscosity in the current ablation blood vessel segment in the blood flow state data, R is the radius of the current ablation blood vessel segment in the vascular structure data, and Re is the Reynolds number of the blood flow in the current ablation blood vessel segment in the blood flow state data; represents taking the minimum value of the calculation results of all ablation blood vessel segments; Exemplarily, the calculation formula for the dynamic blood flow resistance state is used to quantify the change in blood flow resistance caused by structural changes in blood vessels during ablation. In this embodiment, the ablation of varicose veins in the patient's lower extremities will cause the blood vessel wall to contract or harden, directly affecting hemodynamic characteristics. This formula combines the Poiseuille's law and the Reynolds number, which not only realizes the description of the relationship between the pressure difference, flow rate, and blood vessel geometric parameters under the laminar blood flow state, but also distinguishes between the laminar and turbulent flow states. By introducing the pressure difference, blood vessel length, blood viscosity, blood vessel radius, and Reynolds number, it can comprehensively reflect the dynamic impact of blood vessel structure changes on blood flow resistance. Specifically, this embodiment evaluates the change in blood flow resistance of the ablated blood vessel segment in real time to help identify abnormal resistance increases caused by ablation operations; by introducing , the influence of the difference in the size of the lower extremity venous blood vessels is eliminated, so that the resistance states of different blood vessel segments can be accurately compared.

[0022] S222. Analyze the degree of energy dissipation due to blood reflux during the ablation of varicose veins in the patient's lower extremities based on the blood flow state data in the ablation area; The calculation formula for the degree of energy dissipation due to blood reflux is: ; In the formula, Nh is the degree of energy dissipation due to blood reflux of the current ablated blood vessel segment during the ablation of varicose veins in the patient's lower extremities, t1 is the ablation duration of the current ablated blood vessel segment during the ablation of varicose veins in the patient's lower extremities, represents the reverse blood pressure at the t-th moment of the current ablated blood vessel segment during the ablation duration in the blood flow state data, represents the forward blood pressure at the t-th moment of the current ablated blood vessel segment during the ablation duration in the blood flow state data; Exemplarily, patients with varicose veins of the lower extremities often suffer from valve insufficiency, resulting in blood reflux. During the ablation process, thermal energy may further damage the valves or blood vessel walls, further exacerbating the reflux. In this embodiment, the calculation formula for the degree of blood reflux energy dissipation quantifies the cumulative dissipation of reflux energy by integrating the change of the ratio of the reflux 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 reflux energy. When the degree of blood reflux energy dissipation is too high, it indicates that the reflux energy is not effectively suppressed, which may lead to postoperative recurrence or an increased risk of thrombosis. By real-time monitoring the degree of blood reflux energy dissipation in this embodiment, the ablation power or duration can be dynamically adjusted to optimize the energy distribution and reduce blood reflux. Further, this embodiment uses the local pressure ratio as a proxy variable for energy loss, simplifying the complex three-dimensional integral calculation while retaining the physical correlation between the reflux energy and the pressure gradient; Exemplarily, during ablation, thermal energy gradually spreads to the surrounding blood vessel walls, and this process of heat conduction further leads to the gradual attenuation of the reflux energy; At the same time, under the continuous heat exposure environment, the blood vessel walls may undergo physiological changes such as sclerosis or contraction, thereby further suppressing the reflux phenomenon; When the ablation surgery is completed, the local blood flow will redistribute again, and this series of processes can reduce the pressure generated by the reflux. Therefore, by introducing , this embodiment realizes the simulation analysis of the natural dissipation process of the reflux energy over time.

[0023] S223. Analyze the abnormal blood flow state during the ablation of varicose veins in the patient's lower extremities according to the dynamic blood flow resistance state and the degree of blood reflux energy dissipation during the ablation of varicose veins in the patient's lower extremities; The calculation formula for the abnormal blood flow state is: ; In the formula, LF is the abnormal blood flow state of the current ablation blood vessel segment during the ablation of varicose veins in the patient's lower extremities, Xz is the dynamic blood flow resistance state of the current ablation blood vessel segment during the ablation of varicose veins in the patient's lower extremities, and Nh is the degree of blood reflux energy dissipation of the current ablation blood vessel segment during the ablation of varicose veins in the patient's lower extremities.

[0024] Exemplarily, this embodiment comprehensively evaluates the abnormal state of hemodynamics in the vascular segment during the ablation process, with a focus on the synergistic effect between dynamic blood flow resistance and the degree of energy dissipation due to blood reflux. Varicose vein ablation may cause vascular wall contraction, sclerosis, or valvular insufficiency. These structural changes will significantly increase blood flow resistance and exacerbate blood reflux. This embodiment introduces a logarithmic function term to smooth the non-linear effect of energy dissipation due to blood reflux, avoiding distortion of the calculation results caused by a sudden surge in energy dissipation due to blood reflux, such as a short-term high pressure difference. At the same time, since the impact of energy dissipation due to blood reflux on blood flow abnormalities is relatively 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.

[0025] In this embodiment, as Figure 4 shown, in step S3, the damage state of the ablation area during the ablation of varicose veins in the patient's lower extremities is analyzed, which specifically includes the following steps: S31. Extract the temperature field data and vascular structure data of the ablation area; S32. Construct an analysis model for the damage state of the ablation area, import the temperature field data and vascular structure data of the ablation area into the analysis model for the damage state of the ablation area, analyze the damage state of the ablation area during the ablation of varicose veins in the patient's lower extremities, and obtain the analysis result of the damage state of the ablation area during the ablation of varicose veins in the patient's lower extremities.

[0026] In this embodiment, the construction process of the analysis model for the damage state of the ablation area in step S32 includes the following specific steps: S321. Based on the temperature field data and vascular structure data of the ablation area, analyze the thermal damage state of the vascular structure in the ablation area during the ablation of varicose veins in the patient's lower extremities; The calculation formula for the thermal damage state of the vascular structure is: ; In the formula, Rs is the degree of dynamic cumulative thermal stress of the currently ablated vascular segment in the ablation area during the ablation of varicose veins in the patient's lower extremities, is the vascular wall density of the currently ablated vascular segment in the vascular structure data, Cp is the specific heat capacity of the vascular wall of the currently ablated vascular segment in the temperature field data of the ablation area, is the ablation temperature of the currently ablated vascular segment at the t-th moment during the ablation duration in the temperature field data of the ablation area, To is the initial temperature of the vascular wall of the currently ablated vascular segment in the temperature field data of the ablation area, is the rate of change of the average temperature of the blood vessel wall of the current ablated blood vessel segment with respect to time in the temperature field data of the ablation region, k is the thermal conductivity of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation region, Ah is the cross-sectional area of the current ablated blood vessel segment in the blood vessel structure data, and d is the thickness of the blood vessel wall of the current ablated blood vessel segment in the blood vessel structure data; Exemplarily, this embodiment is used to quantify the cumulative degree of structural damage to the blood vessel wall caused by thermal energy during varicose vein ablation. The ablation surgery denatures the blood vessel wall proteins and contracts the collagen fibers through thermal energy, thereby achieving blood vessel closure. However, excessive heat exposure may lead to blood vessel perforation or irreversible scarring. Therefore, this embodiment can capture the cumulative thermal stress during the entire ablation cycle through an integral form. Specifically, the blood vessel wall density and specific heat capacity determine the ability of unit volume tissue to absorb thermal energy; the thermal conductivity can reflect the diffusion efficiency of heat along the blood vessel wall; the cross-sectional area and wall thickness characterize the regulation of heat distribution by the blood vessel geometry. 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 rate of temperature change 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 increase, which may cause instantaneous thermal shock and exacerbate blood vessel tissue damage under a high temperature difference.

[0027] S322. Analyze the dynamic cumulative degree of thermal stress in the ablation region during the ablation of varicose veins in the patient's lower extremities based on the temperature field data of the ablation region and the blood vessel structure data; The calculation formula for the dynamic cumulative degree of thermal stress is: ; In the formula, Rd is the dynamic cumulative degree of thermal stress of the current ablated blood vessel segment in the ablation region during the ablation of varicose veins in the patient's lower extremities, a is the coefficient of thermal expansion of the blood vessel wall of the current ablated blood vessel segment in the blood vessel structure data, E is the elastic modulus of the current ablated blood vessel segment in the blood vessel structure data, Qx is the yield strength of the blood vessel wall of the current ablated blood vessel segment in the blood vessel structure data, is the temperature gradient of the blood vessel wall at the t-th moment during the ablation duration of the current ablated blood vessel segment in the temperature field data of the ablation region; Exemplarily, this embodiment is used to quantify the dynamic cumulative effect of thermal stress caused by temperature gradient in the blood vessel wall during the ablation process. When performing microwave ablation on varicose veins in the lower extremities of a patient, heat energy is transferred to the blood vessel wall through a catheter, resulting in an increased local temperature gradient, which in turn causes thermal expansion and mechanical stress. The accumulation of thermal stress is closely related to the blood vessel geometry and the temperature field. In this embodiment, the calculation formula for the degree of dynamic cumulative thermal stress captures the stress accumulation throughout the ablation cycle through an integral form, and at the same time introduces a denominator term as a normalization factor to eliminate the influence of individual blood vessel differences, ensuring that this embodiment can be applied to the blood vessel characteristics of different patients. Specifically, the degree of dynamic cumulative thermal stress in this embodiment reflects the risk of fatigue damage to the blood vessel wall caused by the thermal expansion and contraction cycle. When the degree of dynamic cumulative thermal stress increases sharply, it may be necessary to adjust the ablation mode of the ablation device to reduce the risk of blood vessel wall cracks or loss of elasticity.

[0028] S323. Analyze the damage state of the ablation area during the ablation of varicose veins in the lower extremities of the patient according to the thermal damage state of the blood vessel structure and the degree of dynamic cumulative thermal stress in the ablation area during the ablation of varicose veins in the lower extremities of the patient; The calculation formula for the damage state of the ablation area is: ; In the formula, is the damage state of the current ablation blood vessel segment in the ablation area during the ablation of varicose veins in the lower extremities of the patient, Rs is the degree of dynamic cumulative thermal stress in the current ablation blood vessel segment in the ablation area during the ablation of varicose veins in the lower extremities of the patient, and Rd is the degree of dynamic cumulative thermal stress in the current ablation blood vessel segment in the ablation area during the ablation of varicose veins in the lower extremities of the patient.

[0029] Exemplarily, this embodiment comprehensively evaluates the blood vessel damage state caused by the combined action of thermal stress and mechanical stress during the ablation process. When performing ablation on varicose veins in the lower extremities of a patient, heat energy not only directly causes tissue degeneration, but also generates thermal expansion stress due to the temperature gradient. The two jointly determine the final degree of blood vessel damage. In this embodiment, the calculation formula for the damage state of the ablation area effectively suppresses the interference of abnormal values that may occur after normalization of the combined action of the two stress mechanisms through the hyperbolic tangent function, so that the damage state is limited within a physically interpretable range, avoiding misjudgment caused by calculation overflow; at the same time, this embodiment also emphasizes the non-linear superposition of thermal stress and mechanical stress through the product of the thermal damage state of the blood vessel structure and the degree of dynamic cumulative thermal stress. For example, high thermal stress may soften the blood vessel wall, thus amplifying the destructive effect of mechanical stress.

[0030] In this embodiment, in step S4, constructing a surgical dynamic risk quantification model includes the following specific steps: S41. Obtain the analysis results of the abnormal blood flow state and the ablation area damage state during the ablation of varicose veins in the patient's lower extremities; S42. Quantify the surgical dynamic risk during the ablation of varicose veins in the patient's lower extremities according to the analysis results of the abnormal blood flow state and the ablation area damage state during the ablation of varicose veins in the patient's lower extremities, and obtain the quantification result of the surgical dynamic risk during the ablation of varicose veins in the patient's lower extremities; The calculation formula for the surgical dynamic risk is: ; In the formula, RT is the surgical dynamic risk of the current ablated blood vessel segment in the ablation area during the ablation of varicose veins in the patient's lower extremities.

[0031] Exemplarily, this embodiment is used to dynamically quantify the surgical risk under the combined action of blood flow abnormality and tissue damage during varicose vein ablation. In the ablation surgery of varicose veins in the lower extremities, the risk of the surgery depends not only on the influence of a single factor and independent action in blood flow abnormality or thermal damage, but also on the non-linear 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 surgical dynamic risk, the numerator term uses the hyperbolic tangent function to compress the geometric mean term to the numerical range of positive and negative one to avoid the overflow of the calculation result caused by extreme values; the denominator term uses , and maps the two to the numerical range of 0-1 to ensure that the final surgical dynamic risk falls within a reasonable range. Further, during the ablation of thin-walled blood vessels, this embodiment can dynamically couple the abnormal blood flow state and the ablation area damage state, and can accurately analyze the situation that a high abnormal blood flow state may affect the ablation area damage state by intensifying the temperature gradient through the change of flow velocity. Further, the use of the hyperbolic tangent function in this embodiment can also 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 the critical damage, so that the technical solution provided by this embodiment not only conforms to the non-linear response characteristics of the biological system, but also provides an operable risk index for clinical real-time decision-making, realizing the accurate quantification of the surgical risk.

[0032] In this embodiment, in step S5, the parameters of the ablation device are dynamically adjusted, which specifically includes: S51. Obtain the maximum value of the quantification results of the surgical dynamic risks of all the ablated blood vessel segments in the ablation area during the ablation of varicose veins in the patient's lower extremities as the ablation surgical dynamic risk threshold, and at the same time obtain the quantification result of the surgical dynamic risk of the current ablated blood vessel segment; S52. When the quantified surgical dynamic risk result of the current ablated blood vessel segment is greater than or equal to the ablation surgical dynamic risk threshold, reduce the microwave output power of the ablation device and simultaneously shorten the microwave output time until the quantified surgical dynamic risk result of the current ablated blood vessel segment is less than the ablation surgical dynamic risk threshold, and then stop dynamically adjusting the parameters of the ablation device; when the quantified surgical dynamic risk result of the current ablated blood vessel segment is less than the ablation surgical dynamic risk threshold, continue the ablation operation.

[0033] Embodiment 2

[0034] As Figure 2 shown, this embodiment provides an ablation process data management system based on big data evaluation, including: A data acquisition module, configured to acquire blood flow state data and blood vessel structure data of the ablation area during the ablation of varicose veins in the lower extremities of a patient, and simultaneously acquire temperature field data of the ablation area; A blood flow abnormal state analysis module, configured to construct a blood flow abnormal state analysis model based on the blood flow state data and blood vessel structure data of the ablation area, and analyze the blood flow abnormal state during the ablation of varicose veins in the lower extremities of a patient; An ablation area damage state analysis module, configured to construct an ablation area damage state analysis model based on the temperature field data of the ablation area and the blood vessel structure data, and analyze the ablation area damage state during the ablation of varicose veins in the lower extremities of a patient; A surgical dynamic risk quantification module, configured to construct a surgical dynamic risk quantification model according to the analysis results of the blood flow abnormal state and the ablation area damage state during the ablation of varicose veins in the lower extremities of a patient, and quantify the surgical dynamic risk during the ablation of varicose veins in the lower extremities of a patient; An ablation device parameter dynamic adjustment module, configured to dynamically adjust the parameters of the ablation device based on the quantified surgical dynamic risk result during the ablation of varicose veins in the lower extremities of a patient; A control module, configured to control the operation of the data acquisition module, the blood flow abnormal state analysis module, the ablation area damage state analysis module, the surgical dynamic risk quantification module, and the ablation device parameter dynamic adjustment module.

[0035] For the steps of the above parameters and each unit module in the ablation process data management system based on big data evaluation of the present invention to implement corresponding functions, reference can be made to the parameters and steps in the embodiments of the ablation process data management method based on big data evaluation in the above text, which will not be elaborated here.

[0036] Embodiment 3

[0037] An electronic device according to an embodiment of the present invention includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes a method for managing ablation process data based on big data evaluation by calling the computer program stored in the memory. It should be noted that: all computer programs of the method for managing ablation process data based on big data evaluation are implemented in the C language. Among them, the data acquisition module, the blood flow abnormal state analysis module, the ablation area damage state 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.

[0038] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0039] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, according to the content of this specification, many modifications and changes can be made. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A data management method for ablation process based on big data evaluation, characterized in that It includes the following steps: S1. Obtain the blood flow state data and vascular structure data of the ablation area during the ablation of varicose veins in the lower extremities of the patient, and at the same time obtain the temperature field data of the ablation area; S2. Based on the blood flow state data and vascular structure data of the ablation area, construct an analysis model for abnormal blood flow states, and analyze the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient; S3. Based on the temperature field data and vascular structure data of the ablation area, construct an analysis model for the injury state of the ablation area, and analyze the injury state of the ablation area during the ablation of varicose veins in the lower extremities of the patient; S4. According to the analysis results of the abnormal blood flow states and the injury state of the ablation area during the ablation of varicose veins in the lower extremities of the patient, construct a dynamic surgical risk quantification model to quantify the dynamic surgical risks during the ablation of varicose veins in the lower extremities of the patient; S5. Based on the quantification results of the dynamic surgical risks during the ablation of varicose veins in the lower extremities of the patient, dynamically adjust the parameters of the ablation device.

2. The ablation process data management method based on big data evaluation according to claim 1, wherein The analysis of the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient in step S2 specifically includes: S21. Extract the blood flow state data and vascular structure data of the ablation area during the ablation of varicose veins in the lower extremities of the patient; S22. Construct an analysis model for abnormal blood flow states, import the blood flow state data and vascular structure data of the ablation area into the analysis model for abnormal blood flow states, analyze the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient, and obtain the analysis results of the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient.

3. The ablation process data management method based on big data evaluation according to claim 2, wherein The construction process of the analysis model for abnormal blood flow states in step S22 specifically includes: S221. Based on the blood flow state data and vascular structure data of the ablation area, analyze the dynamic blood flow resistance state during the ablation of varicose veins in the lower extremities of the patient; S222. Based on the blood flow state data of the ablation area, analyze the degree of energy dissipation of blood countercurrent during the ablation of varicose veins in the lower extremities of the patient; The calculation formula for the degree of energy dissipation of blood countercurrent is: ； Where, Nh is the degree of blood reverse energy dissipation of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient, and t1 is the ablation duration of the current ablated blood vessel segment during the ablation of varicose veins in the lower extremities of the patient. represents the reverse blood pressure at the t-th moment during the ablation duration of the current ablated blood vessel segment in the blood flow state data. represents the forward blood pressure at the t-th moment during the ablation duration of the current ablated blood vessel segment in the blood flow state data. S223. According to the dynamic blood flow resistance state and the degree of energy dissipation of blood countercurrent during the ablation of varicose veins in the lower extremities of the patient, analyze the abnormal blood flow states during the ablation of varicose veins in the lower extremities of the patient; The calculation formula for the abnormal blood flow state is: ； In the formula, LF is the abnormal blood flow state of the current ablation vascular segment during the ablation of varicose veins in the lower extremities of the patient, Xz is the dynamic blood flow resistance state of the current ablation vascular segment during the ablation of varicose veins in the lower extremities of the patient, and Nh is the degree of energy dissipation of blood countercurrent of the current ablation vascular segment during the ablation of varicose veins in the lower extremities of the patient.

4. The ablation process data management method based on big data evaluation according to claim 3, wherein, The analysis of the injury state of the ablation area during the ablation of varicose veins in the lower extremities of the patient in step S3 specifically includes the following steps: S31. Extract the temperature field data and vascular structure data of the ablation area; 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 ablation of the patient's lower extremity varicose veins, and obtain the analysis result of the ablation area damage state during the ablation of the patient's lower extremity varicose veins.

5. The ablation process data management method based on big data evaluation according to claim 4, wherein The construction process of the ablation area damage state analysis model in step S32 includes the following specific steps: S321. Based on the ablation area temperature field data and vascular structure data, analyze the thermal damage state of the vascular structure in the ablation area during the ablation of the patient's lower extremity varicose veins; The calculation formula for the thermal damage state of the vascular structure is: ； Wherein, Rs is the dynamic cumulative degree of thermal stress of the current ablated blood vessel segment in the ablation area during the ablation of varicose veins in the patient's lower extremities, is the blood vessel wall density of the current ablated blood vessel segment in the blood vessel structure data, Cp is the specific heat capacity of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation area, is the ablation temperature at the t-th moment during the ablation duration of the current ablated blood vessel segment in the temperature field data of the ablation area, To is the initial temperature of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation area, is the change rate of the average temperature of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation area with time, k is the thermal conductivity of the blood vessel wall of the current ablated blood vessel segment in the temperature field data of the ablation area, Ah is the cross-sectional area of the current ablated blood vessel segment in the blood vessel structure data, and d is the thickness of the blood vessel wall of the current ablated blood vessel segment in the blood vessel structure data; S322. Based on the ablation area temperature field data and vascular structure data, analyze the dynamic cumulative degree of thermal stress in the ablation area during the ablation of the patient's lower extremity varicose veins; S323. According to the thermal damage state of the vascular structure and the dynamic cumulative degree of thermal stress in the ablation area during the ablation of the patient's lower extremity varicose veins, analyze the ablation area damage state during the ablation of the patient's lower extremity varicose veins; The calculation formula for the ablation area damage state is: ； In the formula, is the ablation area damage state of the current ablation blood vessel segment during the ablation of varicose veins in the lower limbs of the patient. Rs is the dynamic cumulative degree of thermal stress of the current ablation blood vessel segment in the ablation area during the ablation of varicose veins in the lower limbs of the patient, and Rd is the dynamic cumulative degree of thermal stress of the current ablation blood vessel segment in the ablation area during the ablation of varicose veins in the lower limbs of the patient.

6. The ablation process data management method based on big data evaluation according to claim 5, wherein The construction of the surgical dynamic risk quantification model in step S4 includes the following specific steps: S41. Obtain the analysis result of the blood flow abnormality state and the analysis result of the ablation area damage state during the ablation of the patient's lower extremity varicose veins; S42. According to the analysis result of the blood flow abnormality state and the analysis result of the ablation area damage state during the ablation of the patient's lower extremity varicose veins, quantify the surgical dynamic risk during the ablation of the patient's lower extremity varicose veins, and obtain the surgical dynamic risk quantification result during the ablation of the patient's lower extremity varicose veins; The calculation formula for the surgical dynamic risk is: ； In the formula, RT is the surgical dynamic risk of the current ablated vascular segment in the ablation area during the ablation of the patient's lower extremity varicose veins.

7. The ablation process data management method based on big data evaluation according to claim 6, characterized in that, The dynamic adjustment of the parameters of the ablation device in step S5 specifically includes: S51. Obtain the maximum value of the surgical dynamic risk quantification results of all completed ablated vascular segments in the ablation area during the ablation of the patient's lower extremity varicose veins as the ablation surgical dynamic risk threshold, and at the same time obtain the surgical dynamic risk quantification result of the current ablated vascular segment; S52. When the surgical dynamic risk quantification result of the current ablated vascular segment is greater than or equal to the ablation surgical dynamic risk threshold, reduce the microwave output power of the ablation device and shorten the microwave output time until the surgical dynamic risk quantification result of the current ablated vascular segment is less than the ablation surgical dynamic risk threshold, and stop the dynamic adjustment of the parameters of the ablation device; when the surgical dynamic risk quantification result of the current ablated vascular segment is less than the ablation surgical dynamic risk threshold, continue the ablation operation.

8. An ablation process data management system based on big data evaluation, which is used to implement the ablation process data management method based on big data evaluation according to any one of claims 1-7, characterized in that The system includes: A data acquisition module for acquiring the blood flow state data and vascular structure data of the ablation area during the ablation of the patient's lower extremity varicose veins, and at the same time acquiring the ablation area temperature field data; A blood flow abnormality state analysis module for constructing a blood flow abnormality state analysis model based on the blood flow state data and vascular structure data of the ablation area, and analyzing the blood flow abnormality state during the ablation of the patient's lower extremity varicose veins; The ablation area damage status analysis module is used to construct an ablation area damage status analysis model based on the ablation area temperature field data and the vascular structure data, and analyze the ablation area damage status during the ablation process of the patient's lower extremity varicose veins; The surgical dynamic risk quantification module is used to construct a surgical dynamic risk quantification model based on the analysis results of the abnormal blood flow status and the ablation area damage status during the ablation process of the patient's lower extremity varicose veins, and quantify the surgical dynamic risk during the ablation process of the patient's lower extremity varicose veins; The ablation device parameter dynamic adjustment module is used to dynamically adjust the parameters of the ablation device based on the quantification result of the surgical dynamic risk during the ablation process of the patient's lower extremity varicose veins; The control module is used to control the operation of the data acquisition module, the abnormal blood flow status analysis module, the ablation area damage status analysis module, the surgical dynamic risk quantification module, and the ablation device parameter dynamic adjustment module.

9. 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 according to any one of claims 1-7 by calling the computer program stored in the memory.

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