Temperature data acquisition and report generation method for cervical cryotherapy

Abstract

The invention belongs to the technical field of cervical cryotherapy, and discloses a cervical cryotherapy temperature data acquisition and report generation method, which comprises the following steps: capturing a temperature gradient based on a multi-point thermosensitive probe array, and generating temperature distribution original data; performing thermal diffusion path analysis to generate freezing front migration characteristic data; positioning a thermal diffusion critical point, and generating freezing depth critical value data; analyzing the interface temperature field to generate a three-dimensional distribution characteristic spectrum; representing an ice crystal formation dynamic state, and generating ice crystallization process data; quantifying the heat conduction effect to generate freezing response data; evaluating tissue damage, and generating depth prediction data; constructing a decision model, and generating regulation and control guidance data; the treatment effect is comprehensively evaluated, and a standardized electronic report is automatically generated; according to the invention, temperature monitoring and individualized scheme optimization in the whole process of cervical cryotherapy are realized, and the therapeutic accuracy and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cervical cryotherapy, and more specifically, to a method for collecting temperature data and generating a report for cervical cryotherapy. Background Art

[0002] With the continuous development of minimally invasive gynecological treatment technology, cervical cryotherapy, as an effective method for treating cervical lesions, has been widely used in clinical practice; however, the current temperature data collection and report generation system during cervical cryotherapy still has obvious deficiencies in refined monitoring and individualized treatment plan formulation.

[0003] In the field of cervical cryotherapy, current temperature monitoring systems often overlook the temperature gradient distribution characteristics at the interface between the cryoprobe and cervical tissue, which has a crucial impact on treatment efficacy and safety. The thermal conductivity characteristics of cervical tissue vary from patient to patient, and cervical tissue density, blood supply, and water content vary from patient to patient, directly affecting the depth and extent of the freezing effect. The lack of real-time, accurate monitoring of the temperature gradient at the interface between the cryoprobe and cervical tissue prevents clinicians from dynamically adjusting freezing parameters based on individual patient characteristics, leading to the risk of under- or over-freezing during treatment. Under-freezing can reduce treatment efficacy and increase the likelihood of lesion recurrence, while over-freezing can damage surrounding normal tissue and cause unnecessary complications, such as cervical stenosis. Furthermore, current post-cervical cryotherapy reporting is mostly manual, lacking an automated, intelligent reporting mechanism. Physicians must manually record treatment parameters, temperature data, and observations during treatment, and then integrate this information into a treatment report. This not only increases the workload of medical staff but can also easily lead to incomplete or inaccurate reports due to human factors, impacting the effectiveness of treatment quality assessment and follow-up management.

[0004] In view of this, the present invention proposes a temperature data collection and report generation method for cervical cryotherapy to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solution: a method for collecting temperature data and generating a report for cervical cryotherapy, comprising:

[0006] Step S1: Capturing the freezing interface temperature gradient based on a multi-point thermal probe array to generate raw data on the temperature distribution of cervical tissue; performing heat diffusion path tracking analysis based on the raw data on the temperature distribution of cervical tissue to generate freezing front migration characteristic data;

[0007] Step S2: locating and identifying the critical point of heat diffusion based on the freezing front migration characteristic data to generate freezing depth critical value data; analyzing the interface temperature field based on the freezing depth critical value data to generate a three-dimensional distribution characteristic map of the freezing zone temperature field;

[0008] Step S3: Dynamically characterize tissue ice crystal formation based on the three-dimensional distribution characteristic map of the freezing zone temperature field to generate tissue ice crystallization process data; quantify the multi-level heat conduction effect based on the tissue ice crystallization process data to generate tissue freezing response channel characteristic data;

[0009] Step S4: Map the tissue damage assessment under different freezing cycles for the tissue freezing response channel characteristic data to generate treatment effect depth prediction data; construct an individualized freezing plan decision model based on the treatment effect depth prediction data to generate freezing parameter dynamic control guidance data; conduct a comprehensive evaluation of the treatment effect based on the freezing parameter dynamic control guidance data and the three-dimensional distribution characteristic map of the freezing zone temperature field, and automatically generate a standardized electronic report for cervical cryotherapy.

[0010] A temperature data collection and report generation device for cervical cryotherapy, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory to enable the wiring harness terminal intelligent assembly device to execute the above-mentioned temperature data collection and report generation method for cervical cryotherapy.

[0011] A computer-readable storage medium stores instructions which, when executed on a computer, enable the computer to execute the temperature data collection and report generation method for cervical cryotherapy.

[0012] The technical effects and advantages of the temperature data collection and report generation method for cervical cryotherapy of the present invention are as follows:

[0013] By identifying critical points of thermal diffusion and analyzing the interface temperature field, the system generates a three-dimensional distribution map of the freezing zone temperature field, providing clinicians with a visual representation of the freezing range and effectively avoiding the issues of insufficient or excessive freezing depth in traditional cryotherapy. Dynamic characterization of tissue ice crystal formation and multi-level quantification of heat conduction effects enable comprehensive monitoring of the tissue microscopic freezing process, identifying potential freezing blind spots, and improving treatment safety and effectiveness. By mapping tissue damage assessments during different freezing cycles, the system predicts the depth of treatment effect, providing physicians with a scientific basis and significantly reducing treatment risk. By constructing an individualized cryotherapy decision model, treatment parameters can be customized to the specific characteristics of the patient's cervical tissue, meeting the precise treatment needs of varying lesion size and tissue characteristics. By assessing lesion-treatment range compatibility and evaluating treatment integrity, freezing parameters can be dynamically adjusted based on real-time monitoring data, ensuring treatment efficacy while maximizing the protection of surrounding healthy tissue. Finally, the automatically generated standardized electronic report integrates data from the entire process, improving the standardization and traceability of medical records. This provides comprehensive technical support for cervical cryotherapy, achieving precision, visualization, and standardization of the treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a method for collecting temperature data and generating a report for cervical cryotherapy according to the present invention;

[0015] Figure 2 This is a schematic diagram of a temperature data collection and report generation system for cervical cryotherapy according to the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] Example 1;

[0018] See also Figure 1 As shown, the temperature data collection and report generation method for cervical cryotherapy described in this embodiment includes:

[0019] Step S1: Capturing the freezing interface temperature gradient based on a multi-point thermal probe array to generate raw data on the temperature distribution of cervical tissue; performing heat diffusion path tracking analysis based on the raw data on the temperature distribution of cervical tissue to generate freezing front migration characteristic data;

[0020] Step S2: locating and identifying the critical point of heat diffusion based on the freezing front migration characteristic data to generate freezing depth critical value data; analyzing the interface temperature field based on the freezing depth critical value data to generate a three-dimensional distribution characteristic map of the freezing zone temperature field;

[0021] Step S3: Dynamically characterize tissue ice crystal formation based on the three-dimensional distribution characteristic map of the freezing zone temperature field to generate tissue ice crystallization process data; quantify the multi-level heat conduction effect based on the tissue ice crystallization process data to generate tissue freezing response channel characteristic data;

[0022] Step S4: Map the tissue damage assessment under different freezing cycles for the tissue freezing response channel characteristic data to generate treatment effect depth prediction data; construct an individualized freezing plan decision model based on the treatment effect depth prediction data to generate freezing parameter dynamic control guidance data; conduct a comprehensive evaluation of the treatment effect based on the freezing parameter dynamic control guidance data and the three-dimensional distribution characteristic map of the freezing zone temperature field, and automatically generate a standardized electronic report for cervical cryotherapy.

[0023] Preferably, the process of executing step S1 may specifically include the following steps:

[0024] The temperature gradient of the freezing interface is captured using a circular distributed thermal probe array to generate raw data on the temperature distribution of cervical tissue;

[0025] Deconstruct the temperature-time curve based on the original data of cervical tissue temperature distribution to generate analytical data on non-uniform cooling characteristics;

[0026] Calculate tissue thermal diffusivity based on non-uniform cooling characteristic analysis data to generate patient-specific thermal conductivity characteristic data;

[0027] Heat diffusion path tracking analysis is performed based on the patient's individualized thermal conductivity characteristics data to generate freezing front migration characteristic data.

[0028] Specifically, a ring-shaped thermosensitive probe array is used to capture the temperature gradient at the cryoprobe interface and generate raw data on the cervical tissue temperature distribution. The ring-shaped thermosensitive probe array is a measurement system consisting of multiple high-precision thermistor probes arranged in a circular geometry around the cervical cryoprobe. These probes are distributed radially and longitudinally around the cryoprobe, forming a three-dimensional monitoring network. Each thermosensitive probe is a medical-grade thermistor, minimizing tissue damage. The sampling frequency is 10 Hz, enabling real-time, continuous, and multi-point monitoring of cervical tissue temperature. During cryotherapy, the ring-shaped thermosensitive probe array simultaneously collects temperature data at different depths and angles around the cryoprobe, capturing temperature gradients and generating raw data on the cervical tissue temperature distribution. The raw data is stored in a time series matrix, with each row representing a time point and each column representing the temperature value at a probe location, forming a complete four-dimensional dataset (three-dimensional spatial coordinates plus the time dimension). Based on the raw cervical tissue temperature distribution data, a temperature-time curve is deconstructed to generate analytical data on the non-uniform cooling characteristics. Temperature-time curve deconstruction processing refers to the process of mathematically analyzing and processing the raw temperature data to extract the dynamic cooling characteristics contained therein. First, the temperature-time data at each probe position is noise filtered, and a combination of sliding average filtering and wavelet transform denoising is used to filter out environmental interference and measurement fluctuations. Second, the filtered temperature-time curve is segmented and fitted, dividing the entire freezing process into three stages: the initial rapid cooling stage, the steady-state cooling stage, and the plateau stage. Different fitting functions are used for different stages, and an exponential function is used for the initial rapid cooling stage: Fitting is performed, where T(t) represents the temperature at time t, T0 is the final reading, A is the temperature amplitude, and τ1 is the characteristic time constant. The steady-state cooling segment is fitted using a modified logarithmic function: T(t) = BC × ln(t + D). Fitting is performed using B, C, and D as fitting parameters. The plateau phase is fitted using a linear function: T(t) = E × t + F, where E is the slow drift rate and F is the plateau temperature. Characteristic parameters of each fitting curve segment, including cooling rate, temperature drop slope, and characteristic time constant, are extracted to form a set of non-uniform cooling characteristic parameters. Finally, based on spatial location information, the characteristic parameters at each probe position are integrated to generate non-uniform cooling characteristic analytical data describing the three-dimensional tissue cooling behavior. The non-uniform cooling characteristic analytical data are represented as multidimensional feature vectors, with each spatial location corresponding to one feature vector. The vector components include parameters such as initial temperature, minimum temperature, time to reach 0°C, cooling rate, and temperature stabilization time, comprehensively describing the spatiotemporal dynamics of the tissue cooling process. Tissue thermal diffusivity is calculated based on the non-uniform cooling characteristic analytical data to generate patient-specific thermal conductivity characteristic data. Thermal diffusivity is a physical quantity that describes the rate at which heat propagates through a substance and is a key indicator of heat conduction properties. Due to individual differences in cervical tissue structure, water content, and blood supply, there are significant differences in the thermal diffusivity of tissues across patients. Therefore, it is necessary to calculate the individualized thermal diffusivity of each patient based on measured temperature data. The calculation process is based on solving the inverse problem of the heat conduction equation. The specific steps are as follows: First, establish the heat conduction control equation for cervical tissue, which is in the following basic form: Where ρ is tissue density, c is specific heat capacity, T is temperature, t is time, k is thermal conductivity, and Q is heat sink term, which represents blood perfusion and metabolic heat. Secondly, the thermal diffusivity is introduced. The control equation is simplified to: Based on the temperature distribution and rate of change in the non-uniform cooling characteristic analysis data, a parameter estimation method is used to determine the thermal diffusion coefficient a. The optimal thermal diffusion coefficient is determined by iteratively minimizing the mean square error between the measured and calculated temperatures. Finally, the thermal diffusion coefficient is expressed as a function of temperature and spatial position a(T, x, y, z), and a complete thermal diffusion coefficient distribution model is established through piecewise linear or polynomial fitting. This calculation process obtains patient-specific thermal conductivity characteristic data, including the thermal diffusion coefficient distribution at different temperature ranges and spatial locations. These data accurately reflect the thermodynamic behavior of the patient's cervical tissue during the freezing process. Heat diffusion path tracing analysis is performed based on the patient-specific thermal conductivity characteristic data to generate freezing front migration characteristics. Heat diffusion path tracing analysis is a technique that combines numerical simulation with measured data to track the propagation path and velocity of the freezing front in tissue during freezing. The freezing front is generally defined as the isothermal surface in the tissue where the temperature reaches 0°C, representing the boundary where tissue freezing begins. The tracking analysis is implemented as follows: First, based on the individualized heat conduction characteristic data obtained above, a numerical heat conduction model is constructed and solved using the finite element method or finite difference method. The meshing strategy uses an adaptive refinement strategy, with mesh points denser in areas with large temperature gradients to improve computational accuracy. Second, the model is calibrated using measured temperature data, and boundary conditions and material parameters are adjusted to maximize the agreement between the model predictions and the measured data. Third, a time-marching calculation is performed using the calibrated model. The temperature field distribution is solved at each time step, and the position and shape of the 0°C isothermal surface are extracted to form the spatiotemporal trajectory of the freezing front. Fourth, the expansion velocity and acceleration of the freezing front in different directions are calculated, and the anisotropic characteristics of the front extension are analyzed. Finally, key characteristic parameters of the freezing front extension process, including maximum expansion depth, expansion rate, and front curvature, are extracted to form a complete freezing front migration characteristic dataset. The freezing front migration characteristic data are stored as a time series, with each time point corresponding to a three-dimensional freezing front shape description and related dynamic parameters, comprehensively characterizing the spatiotemporal dynamic characteristics of ice crystal growth during freezing. These data provide important basis for subsequent evaluation of cryotherapy effects and optimization of treatment plans.

[0029] Preferably, the process of executing step S2 may specifically include the following steps:

[0030] The temperature contour change rate is calculated based on the freezing front migration characteristic data to generate the freezing rate gradient change data;

[0031] Accurately locate the tissue freezing transition point based on the freezing rate gradient change data and generate freezing depth critical value data;

[0032] Use high-precision thermal imaging technology to scan the temperature field of the freezing depth critical value data to generate high-density sampling data of deep temperature distribution;

[0033] Based on high-density sampling data of deep temperature distribution, the freezing zone boundary is accurately depicted and quantified to generate a freezing range boundary map;

[0034] The interface temperature field is analyzed according to the freezing range boundary map to generate a three-dimensional distribution characteristic map of the freezing zone temperature field.

[0035] Specifically, the temperature contour change rate is calculated based on the freezing front migration characteristic data to generate freezing rate gradient change data. The temperature contour change rate refers to the displacement speed of a specific temperature contour over time, reflecting the dynamic characteristics of the temperature field change during the freezing process. The calculation process includes: extracting the position information of multiple key temperature contours (such as 0℃, -10℃, etc.) from the freezing front migration characteristic data to form a time series {X1(t),...,X n (t)}, where X1(t) represents the position coordinate of the first temperature contour at time t, X n(t) represents the position coordinate set of the nth temperature contour line at time t. Secondly, the displacement of the contour line position between adjacent time points is calculated based on the position coordinates and divided by the time interval to obtain the instantaneous velocity. The spatial gradient of the temperature contour line velocity is calculated at each point in space to reflect the spatial variation trend of the freezing rate. Combining the time and space dimensions, a four-dimensional tensor field of the freezing rate gradient is constructed to comprehensively describe the spatiotemporal variation characteristics of the freezing rate at each point during the freezing process. The freezing rate gradient variation data contains complete information on the temperature drop rate and its variation trend at each point in the tissue. It can effectively identify areas of uneven rate during the freezing process and provide a basis for subsequent freezing depth analysis. The freezing rate gradient variation data is used to accurately locate the tissue freezing transition point and generate freezing depth critical value data. The tissue freezing transition point refers to the critical position where the tissue transitions from an unfrozen state to a frozen state and is a key indicator for evaluating the effective depth of cryotherapy. Based on freezing rate gradient data, regions with significant changes in the freezing rate gradient are identified. These regions typically correspond to boundaries where tissue properties or structures change. Secondly, a phase transition detection algorithm is applied to analyze the plateau phenomenon in the temperature-time curve, identify the latent heat release phase, and determine the location of the phase transition point. A freezing transition criterion is defined by combining freezing rate and temperature threshold criteria: regions with temperatures below a preset temperature threshold and a freezing rate gradient change rate greater than the preset threshold are marked as frozen regions. A three-dimensional edge detection algorithm is used to accurately extract the boundary between frozen and unfrozen regions, namely the freezing depth critical surface. Finally, the maximum, average, and minimum distances between the freezing depth critical surface and the cryoprobe surface are calculated to form a freezing depth critical value dataset. The freezing depth critical value data represents the effective freezing boundary as a three-dimensional point cloud. Each point contains spatial coordinates and corresponding attributes such as temperature and freezing rate, comprehensively describing the effective range and depth distribution of cryotherapy. High-precision thermal imaging technology is used to scan the freezing depth critical value data for temperature fields, generating high-density sampling data of deep temperature distribution. High-precision thermal imaging technology uses a medical infrared thermal imager combined with a specialized optical system to obtain high-resolution images of the temperature distribution at the tissue surface and superficial layers.Because conventional thermal imaging techniques struggle to directly obtain deep tissue temperature information, this embodiment employs an innovative deep temperature reconstruction method: First, a medical-grade infrared thermal imager is used to perform high-frame-rate thermal imaging of the cervical surface to obtain surface temperature distribution. Second, a mapping model for surface and deep temperatures is established, combining the freezing depth threshold data obtained in the previous steps. This model is based on a modified biological heat conduction equation and a machine learning algorithm, trained with extensive clinical validation data. Third, the mapping model is applied to perform deep extrapolation calculations on the surface thermal imaging data to reconstruct temperature distributions at different depths. Fourth, the discrete point temperature data measured by the thermosensitive probe is fused with the model calculation results, and a continuous three-dimensional temperature field distribution is generated using Kriging interpolation. Finally, the fused temperature field is subjected to high-density sampling to generate high-density sampling data for the deep temperature distribution. This data is stored as a voxel grid, with each voxel recording the precise three-dimensional coordinates and corresponding temperature value, providing a high-precision, high-density description of the temperature distribution within the tissue. Temperature gradient vectors are calculated based on the high-density sampling data for the deep temperature distribution to generate temperature gradient vector field data. The temperature gradient vector is a physical quantity that describes the rate and direction of change of temperature space. For the temperature field T(x,y,z) at a point (x,y,z), its gradient vector is defined as. The calculation process employs a high-precision numerical differentiation method and includes the following steps: First, local surface fitting is performed on the densely sampled data, using cubic spline interpolation to ensure continuity and smoothness of the temperature field. Second, the spatial partial derivatives of the temperature are calculated at each grid point using central differencing or higher-order differencing schemes to construct a temperature gradient vector. The modulus of the temperature gradient vector is calculated to characterize the rate of temperature change. Local maxima of the temperature gradient modulus are identified, which typically correspond to freezing peaks, i.e., areas with the most pronounced freezing effect. The temporal rate of temperature change at each sampling point is calculated and, combined with the spatial gradient information, a complete temperature gradient vector field is generated, including freezing peak point distribution data and temperature reduction rate quantification data. The temperature gradient vector field data not only describes the spatial non-uniformity of the temperature distribution but also reflects the intensity distribution of the freezing effect, providing key information for accurately delineating the boundaries of the freezing region. Based on the freezing peak point distribution data, low-temperature region clustering analysis is performed to generate freezing cluster region data. Low-temperature region clustering analysis is the process of grouping points with similar temperature characteristics into the same category in order to identify the structural characteristics of the freezing region. Based on the freezing peak point distribution data, cluster feature vectors were constructed, including multidimensional features such as the point's temperature value, temperature gradient modulus, and distance from the freezing source. Cluster analysis was performed using a clustering algorithm, including K-means and hierarchical clustering algorithms, to identify cluster regions. Feature statistics were calculated for each cluster region to obtain regional characteristics, including volume, average temperature, temperature standard deviation, and shape indicators (such as ductility). Finally, cluster regions were classified according to preset feature thresholds to generate multi-level freezing cluster region data. The freezing cluster region data was presented in a hierarchical structure, clearly demonstrating the spatial distribution pattern and internal structural characteristics of the freezing regions. Based on the freezing cluster region data and the quantitative temperature reduction rate data, potential unfrozen regions were identified and freezing blind spot warning data was generated. Potential unfrozen regions are areas within the freezing range where the temperature does not reach the effective treatment threshold due to various factors (such as proximity to heat sources and vascular thermal effects). These areas are potential risk points for treatment failure and require key identification and early warning. The identification process includes the following steps: Based on the frozen cluster region data, the convex hull of the frozen region is constructed as the theoretical maximum freezing range; the temperature reduction rate quantitative data is analyzed to identify areas with significantly lower temperature reduction rates than surrounding areas, which may be affected by heat sources; morphological image processing techniques are applied to detect areas within the frozen region that are surrounded by low-temperature areas but have relatively high temperatures; the likelihood of these areas reaching effective treatment temperatures is assessed; and the identified potential unfrozen areas are risk-rated to generate freezing blind spot warning data. The warning data contains information such as the location, size, shape, and risk level of the unfrozen areas, providing important reference for physicians to adjust treatment strategies. Based on the freezing blind spot warning data, high-density sampling data of deep temperature distribution is quantitatively graded to generate a freezing range boundary map.Quantitative grading is the process of converting continuous temperature distribution data into discrete treatment effect grade zones, facilitating intuitive assessment of treatment scope and efficacy. The processing steps include: determining temperature grading standards based on the mechanism of cryoinjury in biological tissues, typically categorizing tissues into complete necrosis (less than or equal to -40°C), high-probability necrosis (-40°C to -20°C), partial damage (-20°C to -8°C), and recoverable (greater than -8°C); establishing a temperature-time comprehensive evaluation model based on freezing time to differentiate between regions with the same temperature but different freezing durations; using freezing blind spot warning data as a correction factor to downgrade the treatment grade of warning areas; applying a three-dimensional isosurface extraction algorithm to extract the boundary surfaces of each graded region and construct a hierarchical model of the freezing range; and using three-dimensional visualization technology to visually display the grading results using multi-color semi-transparent rendering to generate a freezing range boundary map. The boundary map displays the spatial distribution of different treatment effect grades in a hierarchical nested structure, while also annotating potential unfrozen areas and treatment weaknesses, providing physicians with an intuitive and accurate visual reference for comprehensively assessing the treatment scope and efficacy. Based on the freezing range boundary map, the interface temperature field is analyzed to generate a three-dimensional distribution characteristic map of the freezing zone temperature field. Interface temperature field analysis is a process of in-depth analysis and characterization of the temperature distribution at the freezing zone boundary and within the freezing zone, aiming to extract key features and patterns of the temperature field. First, a mathematical morphological analysis is performed on the freezing range boundary map to extract geometric features such as the principal axis direction, symmetry, and curvature distribution. Second, an adaptive coordinate system is established to transform the complex three-dimensional temperature field into a more easily analyzable regular form, such as spherical or cylindrical coordinates. Third, within this new coordinate system, the temperature field is decomposed at multiple scales, and temperature distribution patterns at different spatial frequencies are extracted using wavelet or Fourier transforms. Fourth, statistical characteristics of the temperature field are calculated, including higher-order statistics such as anisotropy index, temperature gradient curvature, temperature distribution skewness, and kurtosis. Finally, based on the extracted characteristic parameters, a three-dimensional distribution characteristic map of the freezing zone temperature field is constructed. This map not only contains information on the spatial distribution of temperature but also on its structural features and patterns. This comprehensive display of the complex structure and characteristics of the freezing zone temperature field provides a rich information foundation for subsequent treatment effect evaluation and freezing mechanism research.

[0036] Preferably, the process of executing step S3 may specifically include the following steps:

[0037] Dynamic characterization of tissue ice crystal formation is performed based on the three-dimensional distribution characteristic map of the freezing zone temperature field, generating tissue ice crystallization process data;

[0038] Analyze the growth pattern of ice crystals inside and outside cells based on the tissue ice crystallization process data to generate microscopic ice crystal morphological characteristics data;

[0039] Multi-level heat conduction effects are quantified based on the microscopic ice crystal morphological characteristic data to generate tissue freezing response channel characteristic data.

[0040] Specifically, the dynamic characterization of tissue ice crystal formation is performed based on the three-dimensional distribution characteristic map of the temperature field in the freezing zone, and the data of the tissue ice crystallization process are generated. The dynamic characterization of tissue ice crystal formation is to convert the macroscopic temperature field information into a model description of the microscopic ice crystal formation process, which is a key link between the physical freezing process and the biological effect. The characterization process first extracts the temperature-time curve of each spatial point based on the three-dimensional distribution characteristic map of the temperature field in the freezing zone; secondly, the improved ice crystal formation kinetic model is applied to convert the temperature data into the ice crystal formation rate and ice crystal volume fraction. The model is based on the modified Avrami equation: VD(t)=1-exp(-N×(t-t0) m), where VD(t) represents the ice crystal integral fraction at time t, N is the ice crystal formation rate constant, t0 is the incubation period, and m is the Avrami index, reflecting the dimensional characteristics of ice crystal growth. Model parameters are calibrated based on tissue type and freezing conditions. For example, for cervical epithelial tissue, the ice crystal formation rate constant ranges from [0.05 to 0.2], and the Avrami index ranges from 2 to 3. Combining tissue water content data with freezing temperature, the theoretical maximum volume fraction of ice crystals in the tissue is calculated as a reference for ice crystallization completeness. Spatial heterogeneity of ice crystal formation is analyzed to identify priority and delayed regions of ice crystal formation. A multidimensional dataset encompassing temporal, spatial, and ice crystal characteristic dimensions is constructed to generate tissue ice crystallization process data. These data, combining time series and spatial distribution, describe the complete process of ice crystal formation, growth, and stabilization in tissue, providing foundational data for subsequent microscopic analysis. Microscopic cryoinjury mechanisms are analyzed based on tissue ice crystallization process data, generating characteristic data for cellular freezing mechanisms. The analysis of microscopic freezing injury mechanisms is based on physical models and biological theories, analyzing the damage processes and mechanisms occurring at the cellular level during freezing. The analysis steps are as follows: Key parameters, including freezing rate, minimum temperature, and hold time, are extracted from tissue ice crystallization data. These parameters directly influence the mechanism and extent of cell freezing. Based on the freezing rate range, the cell freezing mechanism is divided into two main types: rapid freezing (greater than 10°C / min, which primarily forms intracellular ice crystals) and slow freezing (less than 10°C / min, which primarily leads to cell dehydration and extracellular ice crystal formation). The probability of intracellular ice crystal formation and the degree of cell dehydration under different freezing conditions are calculated using a modified Mazur two-factor damage model. This model considers factors such as cell membrane water permeability, cell volume, and surface area ratio, and describes the processes of intracellular water molecule migration and ice nucleation formation through a system of differential equations. Tissue-type-specific parameters, such as membrane properties and water content differences between different cervical cell types, are incorporated to refine the damage mechanism prediction model. Finally, characteristic data for the cell freezing mechanism are generated, including parameters such as the proportion of intracellular ice crystal formation, the proportion of extracellular ice crystal formation, and the expected cell survival rate. These data constitute a quantitative description of the freezing injury mechanism at the microscopic level and provide a theoretical basis for the subsequent ice crystal morphology analysis. The tissue dehydration effect is analyzed based on the tissue ice crystallization process data to generate cell osmotic pressure change characteristic data. The tissue dehydration effect is a phenomenon in which the formation of extracellular ice crystals during freezing causes the extracellular fluid to concentrate, resulting in an osmotic pressure difference between the inside and outside of the cell, and prompting the outflow of intracellular water. Based on the ice crystallization process data, the ice crystal volume fraction at different time points and different spatial positions is calculated, and the extracellular fluid concentration multiple is inferred accordingly; the cell osmotic balance model is applied to calculate the cell volume change and the intracellular solute concentration change. The model equation is: Where V is the current cell volume, V0 is the initial cell volume, θ0 is the initial osmotic pressure, θ is the current osmotic pressure, and VB is the proportion of the cytoskeleton volume not involved in osmosis. The impact of changes in cellular osmotic pressure on cell structure and function was assessed, including the risk of protein denaturation, the degree of cell membrane damage, and the impact on organelle function. The dynamic process of osmotic pressure changes was analyzed by incorporating time, with particular attention paid to the peak osmotic pressure and duration. Cellular osmotic pressure variation characteristic data were generated, including parameters such as maximum osmotic pressure, minimum cell volume, dehydration rate, and dehydration duration. These data comprehensively describe the changing characteristics of the cellular osmotic environment during freezing and are important for understanding the mechanisms of cryoinjury and predicting treatment outcomes. Based on the cell freezing mechanism characteristic data and the cell osmotic pressure variation characteristic data, an ice crystal growth pattern classification matrix was constructed to generate an ice crystal morphology evolution matrix. The ice crystal growth pattern classification matrix provides a theoretical framework for systematically classifying ice crystal formation and development patterns under different freezing conditions. The construction steps are as follows: First, the dimensions of the classification matrix are determined, typically including three main dimensions: freezing rate, minimum temperature, and holding time. Second, freezing rate is classified into five levels: ultra-low, low, medium, high, and ultra-high; minimum temperature is divided into four levels: mild, moderate, severe, and extremely severe; and holding time is divided into three levels: short, medium, and long. Combining data on cell freezing mechanisms and cell osmotic pressure changes, each cell in the matrix (i.e., a specific freezing rate, minimum temperature, and holding time combination) is assigned a corresponding ice crystal growth pattern type, mainly including intracellular dominant, extracellular dominant, and mixed types. The characteristic parameters of each ice crystal growth pattern are further refined, including ice crystal size distribution, morphological characteristics, and growth rate. Finally, an ice crystal morphology evolution matrix is ​​generated. This matrix is ​​a multidimensional data structure that describes the evolutionary patterns and characteristics of ice crystal morphology under different freezing condition combinations, providing a systematic theoretical framework for microscopic ice crystal morphology analysis. Based on the ice crystal morphology evolution matrix, intracellular and extracellular ice crystal growth patterns are analyzed to generate microscopic ice crystal morphology characteristic data. The analysis of ice crystal growth patterns inside and outside cells is based on theoretical models and empirical data to infer the process of formation and development characteristics of microscopic ice crystals during actual freezing.The analysis steps are as follows: First, the parameters of the actual freezing process (freezing rate, minimum temperature, and holding time) are mapped into an ice crystal morphology evolution matrix to determine the corresponding ice crystal growth pattern type. Second, based on the selected growth pattern type, an ice crystal morphology evolution model is applied to simulate the complete process from ice crystal formation to stability. This model, based on a modified phase field theory, considers the influence of factors such as interfacial energy, heat flux density, and latent heat on ice crystal morphology. Third, the number density, size distribution, and morphological characteristics of ice crystals inside and outside the cell are calculated, with particular attention paid to micromorphological features such as ice crystal branching, tip curvature, and growth direction. Fourth, the impact of ice crystal morphology on the degree of tissue damage is evaluated, including mechanical compression effects, cell structure damage effects, and potential damage risk during rewarming. Finally, microscopic ice crystal morphological characteristic data are generated, including parameters such as ice crystal type distribution, average size, morphological complexity index, and damage potential score. These data describe the formation and development characteristics of ice crystals in tissues from a microscopic perspective, providing a microscopic basis for understanding the biological effects of cryotherapy. The microscopic ice crystal morphological characteristic data are subjected to micro-macro freezing effect correlation analysis under the influence of tissue blood supply factors to generate cross-scale freezing effect linkage characteristic data. Micro-macro freezing effect correlation analysis is the process of studying the interactive relationship between microscopic ice crystal formation and macroscopic tissue response, with special attention to the regulatory role of blood supply factors. The microscopic ice crystal morphological characteristic data and tissue vascular distribution data are integrated to establish a spatial correspondence. The vascular distribution data can be obtained through angiography, Doppler ultrasound or biomedical image analysis; secondly, a multi-scale heat conduction model is constructed, which regards the tissue as a multi-phase composite medium, including cell phase, extracellular fluid phase and vascular phase, and considers the heat exchange and material migration between the phases; the blood flow heat sink effect parameter Wb is introduced to quantify the heat carried away by blood flow in unit volume of tissue. The calculation formula is: Qb = Wb × cb × ρb × (Ta-T), Where Qb is the blood flow heat sink term, cb is the blood specific heat capacity, ρb is the blood density, Ta is the arterial blood temperature, and T is the local tissue temperature. The study analyzes the effects of blood flow on ice crystal formation, including reducing the freezing rate, forming a temperature gradient barrier, and accelerating rewarming. The mechanisms of vascular damage caused by microscopic ice crystal formation are assessed, including endothelial cell freezing, thrombosis, and secondary ischemic injury. Finally, cross-scale freezing effect linkage characteristic data are generated, including a vascular density-freezing efficiency relationship, a microvascular occlusion probability distribution, and tissue ischemic area prediction. These data establish a bridge between microscopic ice crystal formation and macroscopic tissue response, revealing the cross-scale transmission mechanism of cryotherapy effects. Based on the cross-scale freezing effect linkage characteristic data, multi-level heat conduction effect quantification is performed to generate tissue freezing response channel characteristic data. Multi-level heat conduction effect quantification is a process that comprehensively analyzes and quantitatively describes the heat transfer phenomena exhibited by tissues at different spatial scales and through different conduction mechanisms during the freezing process.The specific steps are as follows: First, based on cross-scale freezing effect linkage characteristic data, the main heat conduction channels in tissue are identified, including direct conduction channels (heat conduction between tissue parenchyma), vascular channels (heat convection mediated by blood flow), and tissue interface channels (heat transfer between different tissue layers). Second, a multi-channel heat conduction network model is established, treating the tissue as a heat conduction network with a complex topological structure. The network nodes represent tissue regions, the edges represent heat conduction paths, and the weights represent heat conduction efficiency. Third, each heat conduction channel is parameterized, and its heat conductivity, heat capacity, and thermal resistance are calculated to establish a channel characteristic database. Fourth, the dynamic characteristics of the heat conduction channels are analyzed, and the impact of ice crystal formation on the structure and function of the heat conduction channels is studied, with particular attention to the changes in the heat conduction network topology caused by vascular occlusion. Fifth, the contribution of each heat conduction channel to the overall freezing effect is evaluated, and key channels and bottlenecks are identified. Finally, channel characteristic data for tissue freezing response are generated, including channel distribution maps, heat flux density distribution, channel response time constants, and freezing sensitivity classification. These data describe the tissue response to freezing from the perspective of a heat conduction network, providing a theoretical basis for the design of personalized freezing protocols and the prediction of treatment effects.

[0041] Preferably, the process of executing step S4 may specifically include the following steps:

[0042] Mapping tissue damage assessments under different freezing cycles based on tissue freezing response channel characteristic data to generate deep prediction data on treatment effects;

[0043] Extract the freezing-thawing cycle impact factors based on the treatment effect depth prediction data, generate the freezing cycle impact quantitative data, and establish the tissue necrosis prediction distribution nodes based on the freezing cycle impact quantitative data;

[0044] Build an individualized cryotherapy decision model based on tissue necrosis prediction distribution nodes and treatment effect depth prediction data, and generate guidance data for dynamic control of cryotherapy parameters;

[0045] Perform matching analysis on the three-dimensional distribution characteristic map of the freezing zone temperature field and the depth prediction data of the treatment effect to generate lesion-treatment range fit evaluation data;

[0046] Comprehensively evaluate the cryotherapy effect based on the lesion-treatment range fit assessment data and the dynamic control guidance data of cryotherapy parameters to generate the treatment integrity evaluation results;

[0047] Based on the treatment integrity evaluation results, the patient's basic information, treatment parameter records, temperature collection data and efficacy prediction are integrated to automatically generate a standardized electronic report for cervical cryotherapy.

[0048] Specifically, tissue damage assessment mapping is performed on the characteristic data of the tissue freezing response channel under different freezing cycles to generate deep prediction data of the treatment effect. Tissue damage assessment mapping is the process of converting physical freezing parameters (temperature, time, etc.) into biological effects (cell survival rate, degree of tissue necrosis, etc.). Based on the characteristic data of the tissue freezing response channel, key assessment parameters are extracted, including minimum temperature, freezing time, freezing rate, and tissue type; and a temperature-time-damage relationship model is constructed. This model is based on the improved Arrhenius integral equation: Where Bd is the damage integral value, Ac is the frequency factor, Ea is the activation energy, R is the gas constant, T is the absolute temperature, and t is the time. The model parameters are calibrated based on a large amount of experimental data and clinical validation data. For cervical tissue, the frequency factor parameter value is 7.39×10 10The activation energy parameter is 66.9. Considering the impact of different freezing cycles (single freezing, double freezing, or multiple freeze-thaw cycles) on tissue damage, a cycle enhancement factor, Kc, is introduced to modify the damage integral calculation. The calculated damage integral value is converted into cell survival rate and tissue necrosis probability. The conversion relationship is usually S = exp(-Bd), where S is the cell survival rate. Based on the necrosis probability distribution, the treatment effect area is divided into a certain necrosis area (necrosis probability greater than 99%), a high probability necrosis area (necrosis probability 90% to 99%), a partial necrosis area (necrosis probability 50% to 90%), and a low effect area (necrosis probability less than 50%). Finally, treatment effect depth prediction data is generated, including a three-dimensional boundary description of each effect area, a necrosis depth distribution surface, and an effect gradient distribution map. These data comprehensively predict the biological effect distribution of cryotherapy, providing a scientific basis for treatment plan optimization and effect evaluation. Based on the treatment effect depth prediction data, freeze-thaw cycle impact factors were extracted to generate quantitative data on the effects of freeze-thaw cycles. This data was then used to establish tissue necrosis prediction distribution nodes. Freeze-thaw cycle impact factor extraction is the process of analyzing the enhanced effects of multiple freeze-thaw cycles on treatment efficacy. The extraction steps were as follows: First, the effect data of single and multiple freezing were separated from the treatment effect depth prediction data for comparative analysis. Second, the effect enhancement ratio of multiple freezing relative to single freezing was calculated, and the cycle enhancement factor was defined as the ratio of the effective therapeutic depth of multiple freezing to the effective therapeutic depth of a single freezing under the same conditions. Third, the relationship between the cycle enhancement factor and freezing parameters (such as the number of cycles, single freezing time, and thawing interval) was analyzed, and a mathematical relationship model was established: μ = μ0 + up × (n-1) × f(t1, t2), where μ is the cycle enhancement factor, μ0 is the baseline value (usually 1), n ​​is the number of cycles, up is the enhancement coefficient, and f(t1, t2) is a function related to the freezing time t1 and the thawing interval t2. Fourth, the biological mechanisms of the cycle enhancement effect were investigated, including rewarming injury (oxygen free radicals and calcium overload generated during thawing) and cumulative endothelial damage. Fifth, quantitative data on the effects of freezing cycles were generated, including effect enhancement curves under different cycle parameters, optimal cycle parameter recommendations, and distribution maps of cycle injury mechanisms. Based on quantitative data on the effects of cryotherapy cycles, spatial interpolation and machine learning methods were used to establish tissue necrosis prediction distribution nodes. These nodes formed the skeleton of the prediction model. Interpolation algorithms generated a continuous necrosis probability distribution field, providing the basic data structure for subsequent decision-making models. Using this tissue necrosis prediction distribution node and the deep prediction data on treatment effects, a personalized cryotherapy decision model was constructed, generating guidance data for the dynamic control of cryotherapy parameters.The traditional cell freeze-thaw damage accumulation theory was improved by introducing time factors, individual difference factors and tissue specificity factors, and establishing a damage accumulation function: Dtotal = ∑wi × Di × Ki, where Dtotal is the total damage degree, Di is the contribution of each damage mechanism, wi is the weight coefficient, and Ki is the individual correction factor; based on the tissue necrosis prediction distribution nodes and treatment effect depth prediction data, an individualized freezing plan decision model was trained. The model adopts a multi-layer artificial neural network structure. The input layer contains variables such as patient characteristics, tissue characteristics and freezing parameters. The hidden layer uses the ReLU activation function, and the output layer predicts the treatment effect index; the model training data is divided into Model parameters were optimized using a mini-batch gradient descent algorithm for the training, validation, and test sets, with early stopping used to prevent overfitting. The model achieved prediction accuracy exceeding 92% on the test set. Based on the trained decision model, a parameter optimization algorithm was constructed to search for the optimal combination of cryotherapy parameters, including cryotherapy temperature, cryotherapy time, number of cycles, and cryoprobe placement, with the goal of maximizing treatment efficacy and minimizing side effects. Finally, dynamic cryotherapy parameter control guidance data was generated, including parameter optimization recommendations, real-time adjustment strategies, and decision points during treatment. These data were presented in tables, curves, and graphical interfaces for easy reference and application by physicians during treatment. This dynamic cryotherapy parameter control guidance data enabled dynamic adjustment of cryotherapy strategies based on real-time feedback during treatment, enabling precise and personalized treatment implementation. A matching analysis was performed between the three-dimensional distribution characteristic maps of the cryotherapy zone temperature field and the deep prediction data for treatment efficacy, generating data for assessing lesion-treatment zone fit. First, the three-dimensional distribution characteristic map of the temperature field in the freezing zone is integrated with the lesion imaging data to establish a unified spatial coordinate system, and precise alignment is achieved through a registration algorithm (such as multimodal image registration based on mutual information); second, based on the depth prediction data of the treatment effect, the effective treatment boundary (usually defined as the boundary of the area with a necrosis probability greater than 90%) is extracted, and the treatment volume Vt is identified; based on the lesion imaging data, the lesion boundary and safety boundary are extracted, and the target volume Vg is identified; fourth, the volume overlap index, including the volume coverage rate, is calculated. and volume consistency index The undertreated area (the portion of the target area not covered by effective treatment) and the overtreated area (the portion of the effective treatment area that exceeds the target area) are analyzed, and their volume and position distribution are calculated; finally, lesion-treatment range fit assessment data are generated, including three-dimensional overlap visualization, overlap index numerical report, and under / over area annotation map and other information. These data intuitively show the spatial relationship between the treatment range and the lesion, providing a basis for treatment effect evaluation and possible supplementary treatment. Based on the lesion-treatment range fit assessment data and the dynamic control guidance data of the freezing parameters, a comprehensive evaluation of the cryotherapy effect is performed to generate a treatment integrity evaluation result. Comprehensive evaluation of treatment effect is a process of comprehensively analyzing the effectiveness, safety, and integrity of cryotherapy from multiple dimensions. The evaluation steps are as follows: First, based on lesion-treatment volume fit assessment data, the geometric completeness of the treatment is evaluated, including coverage metrics, margin conformity, and safety margin adequacy. Second, based on dynamic cryotherapy parameter control guidance data, the parameter execution completeness of the treatment is evaluated, and the conformity of the actual execution parameters with the planned parameters is compared. Third, the biological completeness of the treatment is assessed, including the expected cell necrosis rate, vascular occlusion effect, and degree of immune response activation. Fourth, individual patient factors such as age, lesion grade, and treatment history are considered to adjust the treatment effect according to individual weights. Fifth, a multidimensional scoring system is established, including coverage completeness (0 to 10 points), parameter execution accuracy (0 to 10 points), expected biological effect (0 to 10 points), and safety score (0 to 10 points). An overall score is calculated by weighted average. Finally, a treatment completeness evaluation result is generated, including a score report, strengths and weaknesses analysis, potential risk warnings, and improvement suggestions. The treatment completeness evaluation results comprehensively reflect the quality of the treatment and provide a basis for decision-making for subsequent follow-up and possible supplemental treatment. Based on the results of treatment integrity evaluation, the patient's basic information, treatment parameter records, temperature collection data, and efficacy prediction are integrated to automatically generate a standardized electronic report for cervical cryotherapy. A standardized report template is established, including five main modules: patient information, treatment parameters, temperature data, effect evaluation, and follow-up recommendations. Basic patient information, including name, age, medical record number, diagnosis, and related examination results, is extracted from the hospital information system. Treatment parameter records, including technical parameters such as cryotherapy equipment model, probe type, freezing temperature curve, freezing time, and number of cycles, are integrated. Statistical analysis results of temperature collection data are summarized, including core data such as minimum temperature distribution, freezing rate distribution, and temperature field characteristics. Treatment integrity evaluation results are integrated, including treatment coverage scores, expected effect assessment, and potential risk analysis. Based on the treatment effect and lesion characteristics, personalized follow-up recommendations are automatically generated, including follow-up time points, recommended examination items, and precautions. Finally, natural language generation technology is applied to convert structured data into a fluent and professional medical description language to generate a standardized electronic report for cervical cryotherapy that meets medical standards.The report is output in PDF format and contains various information presentation forms such as text descriptions, data tables, temperature curve graphs, and three-dimensional visualization graphs. It supports electronic signatures and medical information system integration for easy archiving and sharing.

[0049] By identifying critical points of thermal diffusion and analyzing the interface temperature field, this system generates a three-dimensional distribution map of the freezing zone temperature field, providing clinicians with a visual representation of the freezing range and effectively avoiding the issues of insufficient or excessive freezing depth in traditional cryotherapy. By dynamically characterizing tissue ice crystal formation and quantifying multi-level heat conduction effects, the system comprehensively monitors the microscopic freezing process of tissues, identifying potential freezing blind spots and improving treatment safety and effectiveness. By mapping tissue damage assessments during different freezing cycles, the system predicts the depth of treatment effect, providing physicians with a scientific basis and significantly reducing treatment risk. By constructing an individualized cryotherapy decision model, treatment parameters can be customized to the specific characteristics of cervical tissue, meeting the precise treatment needs of different lesion sizes and tissue characteristics. By assessing lesion-treatment range compatibility and evaluating treatment integrity, freezing parameters can be dynamically adjusted based on real-time monitoring data, ensuring treatment efficacy while maximizing the protection of surrounding healthy tissue. Finally, the automatically generated standardized electronic report integrates data from the entire process, improving the standardization and traceability of medical records. This provides comprehensive technical support for cervical cryotherapy, achieving precision, visualization, and standardization of the treatment process.

[0050] Example 2;

[0051] See also Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description of Example 1. A temperature data collection and report generation system for cervical cryotherapy is provided, comprising:

[0052] Data acquisition and analysis module: Based on the multi-point thermal probe array, the temperature gradient of the freezing interface is captured to generate the original data of the temperature distribution of cervical tissue; based on the original data of the temperature distribution of cervical tissue, the heat diffusion path is tracked and analyzed to generate the characteristic data of the migration of the freezing front;

[0053] Positioning and analysis module: locates and identifies the critical point of thermal diffusion based on the freezing front migration characteristic data, and generates freezing depth critical value data; analyzes the interface temperature field based on the freezing depth critical value data, and generates a three-dimensional distribution characteristic map of the freezing zone temperature field;

[0054] Temperature quantification module: Dynamically characterizes tissue ice crystal formation based on the three-dimensional distribution characteristic map of the freezing zone temperature field, generating tissue ice crystallization process data; quantifies multi-level heat conduction effects based on the tissue ice crystallization process data, generating tissue freezing response channel characteristic data;

[0055] Treatment effect evaluation module: Map the tissue damage assessment under different freezing cycles based on the tissue freezing response channel characteristic data to generate deep prediction data of treatment effect; build an individualized freezing plan decision model based on the deep prediction data of treatment effect, and generate guidance data for dynamic control of freezing parameters; conduct a comprehensive evaluation of treatment effects based on the dynamic control guidance data of freezing parameters and the three-dimensional distribution characteristic map of the freezing zone temperature field, and automatically generate a standardized electronic report for cervical cryotherapy.

[0056] This embodiment also provides a temperature data collection and report generation device for cervical cryotherapy, and the wiring harness terminal intelligent assembly device includes a memory and a processor, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the temperature data collection and report generation method for cervical cryotherapy in the above-mentioned embodiments.

[0057] This embodiment also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the temperature data collection and report generation method for cervical cryotherapy.

[0058] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will be able to modify the technical solutions described in the foregoing embodiments or to substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0059] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0060] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0062] In the description of the present invention, “several” means one or more, and “a large number” means two or more.

[0063] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above 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.

[0064] The formulas in this manual are all dimensionless and calculated using numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field based on actual conditions.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for collecting temperature data and generating reports for cervical cryotherapy, characterized in that: include: Step S1: Capturing the freezing interface temperature gradient based on a multi-point thermal probe array to generate raw data on the temperature distribution of cervical tissue; performing heat diffusion path tracking analysis based on the raw data on the temperature distribution of cervical tissue to generate freezing front migration characteristic data; Step S2: locating and identifying the critical point of heat diffusion based on the freezing front migration characteristic data to generate freezing depth critical value data; analyzing the interface temperature field based on the freezing depth critical value data to generate a three-dimensional distribution characteristic map of the freezing zone temperature field; Step S3: Dynamically characterize the formation of tissue ice crystals based on the three-dimensional distribution characteristic map of the freezing zone temperature field, and generate tissue ice crystallization process data; Quantify the multi-level heat conduction effect based on the tissue ice crystallization process data to generate tissue freezing response channel characteristic data; Step S4: Map the tissue damage assessment under different freezing cycles for the tissue freezing response channel characteristic data to generate treatment effect depth prediction data; construct an individualized freezing plan decision model based on the treatment effect depth prediction data to generate freezing parameter dynamic control guidance data; conduct a comprehensive evaluation of the treatment effect based on the freezing parameter dynamic control guidance data and the three-dimensional distribution characteristic map of the freezing zone temperature field, and automatically generate a standardized electronic report for cervical cryotherapy.

2. The temperature data collection and report generation method for cervical cryotherapy according to claim 1, characterized in that: Step S1 includes: The temperature gradient of the freezing interface is captured using a circular distributed thermal probe array to generate raw data on the temperature distribution of cervical tissue; Deconstruct the temperature-time curve based on the original data of cervical tissue temperature distribution to generate analytical data on non-uniform cooling characteristics; Calculate tissue thermal diffusivity based on non-uniform cooling characteristic analysis data to generate patient-specific thermal conductivity characteristic data; Heat diffusion path tracking analysis is performed based on the patient's individualized thermal conductivity characteristics data to generate freezing front migration characteristic data.

3. The temperature data collection and report generation method for cervical cryotherapy according to claim 2, characterized in that: Step S2 includes: The temperature contour change rate is calculated based on the freezing front migration characteristic data to generate the freezing rate gradient change data; Accurately locate the tissue freezing transition point based on the freezing rate gradient change data and generate freezing depth critical value data; Use high-precision thermal imaging technology to scan the temperature field of the freezing depth critical value data to generate high-density sampling data of deep temperature distribution; Based on high-density sampling data of deep temperature distribution, the freezing zone boundary is accurately depicted and quantified to generate a freezing range boundary map; The interface temperature field is analyzed according to the freezing range boundary map to generate a three-dimensional distribution characteristic map of the freezing zone temperature field.

4. The method for collecting temperature data and generating a report for cervical cryotherapy according to claim 3, characterized in that: The method of accurately depicting and quantifying the freezing zone boundary based on the high-density sampling data of deep temperature distribution and generating a freezing range boundary map includes: Performing temperature gradient vector calculation based on high-density sampling data of deep temperature distribution to generate temperature gradient vector field data, wherein the temperature gradient vector field data includes freezing peak point distribution data and temperature reduction rate quantification data; Perform low-temperature area cluster analysis based on freezing peak point distribution data to generate freezing cluster area data; Based on the freezing cluster area data and the quantitative data of temperature reduction rate, potential unfrozen areas are identified to generate freezing dead zone warning data; Based on the freezing dead zone warning data, the high-density sampling data of deep temperature distribution are quantitatively graded to generate a freezing range boundary map.

5. The temperature data collection and report generation method for cervical cryotherapy according to claim 4, characterized in that: Step S3 includes: Dynamic characterization of tissue ice crystal formation is performed based on the three-dimensional distribution characteristic map of the freezing zone temperature field, generating tissue ice crystallization process data; Analyze the growth pattern of ice crystals inside and outside cells based on the tissue ice crystallization process data to generate microscopic ice crystal morphological characteristics data; Multi-level heat conduction effects are quantified based on the microscopic ice crystal morphological characteristic data to generate tissue freezing response channel characteristic data.

6. The method for temperature data collection and report generation for cervical cryotherapy according to claim 5, characterized in that: The analysis of ice crystal growth patterns inside and outside cells based on tissue ice crystallization process data to generate microscopic ice crystal morphological characteristic data includes: Analyze the microscopic freezing damage mechanism based on the tissue ice crystallization process data to generate cell freezing mechanism characteristic data; Analyze tissue dehydration effects based on tissue ice crystallization process data to generate cell osmotic pressure change characteristic data; Based on the characteristic data of cell freezing mechanism and the characteristic data of cell osmotic pressure change, the ice crystal growth pattern classification matrix is ​​constructed to generate the ice crystal morphology evolution matrix; The ice crystal growth patterns inside and outside the cells are analyzed based on the ice crystal morphology evolution matrix to generate microscopic ice crystal morphology characteristic data.

7. The temperature data collection and report generation method for cervical cryotherapy according to claim 6, characterized in that: The multi-level heat conduction effect quantification based on the microscopic ice crystal morphological characteristic data to generate tissue freezing response channel characteristic data includes: The microscopic ice crystal morphological characteristic data were used to conduct micro-macroscopic freezing effect correlation analysis under the influence of tissue blood supply factors, and cross-scale freezing effect linkage characteristic data were generated; Multi-level heat conduction effects are quantified based on the cross-scale freezing effect linkage characteristic data to generate tissue freezing response channel characteristic data.

8. The temperature data collection and report generation method for cervical cryotherapy according to claim 7, characterized in that: Step S4 includes: Mapping tissue damage assessments under different freezing cycles based on tissue freezing response channel characteristic data to generate deep prediction data on treatment effects; Extract the freezing-thawing cycle impact factors based on the treatment effect depth prediction data, generate the freezing cycle impact quantitative data, and establish the tissue necrosis prediction distribution nodes based on the freezing cycle impact quantitative data; Build an individualized cryotherapy decision model based on tissue necrosis prediction distribution nodes and treatment effect depth prediction data, and generate guidance data for dynamic control of cryotherapy parameters; Perform matching analysis on the three-dimensional distribution characteristic map of the freezing zone temperature field and the depth prediction data of the treatment effect to generate lesion-treatment range fit evaluation data; Comprehensively evaluate the cryotherapy effect based on the lesion-treatment range fit assessment data and the dynamic control guidance data of cryotherapy parameters to generate the treatment integrity evaluation results; Based on the treatment integrity evaluation results, the patient's basic information, treatment parameter records, temperature collection data and efficacy prediction are integrated to automatically generate a standardized electronic report for cervical cryotherapy.

9. A temperature data acquisition and report generation device for cervical cryotherapy, characterized in that: The wiring harness terminal intelligent assembly device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory so that the wiring harness terminal intelligent assembly device executes the temperature data collection and report generation method for cervical cryotherapy as described in any one of claims 1-8.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the temperature data collection and report generation method for cervical cryotherapy as described in any one of claim 187 is implemented.

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