Intelligent medical dental light curing machine real-time monitoring method and system

Abstract

The invention relates to the technical field of photocuring monitoring, in particular to an intelligent medical dental photocuring machine real-time monitoring method and system. The method comprises the following steps: carrying out resin surface light intensity value calculation on a target dental light-cured area to generate initial surface light intensity distribution data; performing resin photosensitive curing prediction on the initial surface light intensity distribution data to generate resin curing degree prediction curve data; performing real-time photoacoustic detection on the target dental photocuring area by using an intelligent dental photocuring machine to generate actual curing degree data of resin; and performing curing degree error calculation on the actual curing degree data of the resin by using the resin curing degree prediction curve data, and performing light source parameter regulation and control to generate light curing quality evaluation data. By dynamically tracking the photocuring error, the real-time monitoring of the dental photocuring process and the intelligent regulation and control of the light source parameters are realized, the problem of insufficient curing or excessive curing in the photocuring process is effectively solved, and the photocuring quality and the treatment efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of light curing monitoring, and in particular to a real-time monitoring method and system for an intelligent medical dental light curing machine. Background Art

[0002] As an efficient and convenient clinical restoration method, dental light curing technology has been widely used in many fields such as caries restoration, tooth whitening, orthodontic treatment, etc. Its principle is to use light of a specific wavelength to irradiate photosensitive resin materials, trigger the photoinitiator to produce free radicals, and then trigger the polymerization reaction of resin monomers, and finally realize the transformation of materials from liquid to solid. The core of light curing technology lies in the performance of the light curing machine. The parameters such as the light intensity, wavelength, and irradiation time directly affect the curing effect of the resin material, and then affect the mechanical properties, biocompatibility and service life of the restoration. The ideal curing effect requires that the resin material can be fully and evenly cured to avoid insufficient or excessive curing. Due to the complex oral environment, the light curing depth, light path, and curing uniformity are difficult to accurately control, and it is easy to have insufficient or excessive curing problems. However, most traditional dental light curing machines adopt an open-loop control method, which only irradiates according to the preset light source parameters and relies on experience to judge the curing time. It is impossible to accurately evaluate the actual curing degree of the resin and it is difficult to achieve precise control. Summary of the invention

[0003] Based on this, the present invention provides a real-time monitoring method and system for an intelligent medical dental light curing machine to solve at least one of the above technical problems.

[0004] To achieve the above object, a real-time monitoring method for an intelligent medical dental light curing machine comprises the following steps:

[0005] Step S1: using an intelligent dental light curing machine to irradiate a target dental light curing area with a target area light source to generate multi-viewing angle light intensity signal data; performing light field matrix processing on the multi-viewing angle light intensity signal data to generate multi-viewing angle light field matrix data;

[0006] Step S2: Acquire resin curing material parameters; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; perform light propagation path analysis on the light field voxel space data using the resin curing material parameters to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data;

[0007] Step S3: processing the initial surface light intensity distribution data for resin surface light intensity distribution by light-curing ray tracing data to generate surface light intensity energy distribution data; predicting resin photosensitization by using resin curing material parameters to generate resin curing degree prediction curve data;

[0008] Step S4: using the intelligent dental light curing machine to perform real-time photoacoustic detection on the target dental light curing area to generate multi-wavelength laser feedback signal data; performing resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin;

[0009] Step S5: Calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data to generate curing degree error curve data; adjust the light source parameters of the intelligent dental light curing machine using the curing degree error curve data, and perform termination light curing quality evaluation to generate light curing quality evaluation data.

[0010] The present invention uses an intelligent dental light curing machine to irradiate the target dental light curing area with a light source, which can ensure that the light evenly covers the area to be treated and provide information on light intensity at different angles, which helps to fully understand the propagation characteristics of light in resin materials. The specific parameters of the resin curing material are obtained, including important properties such as light sensitivity and refractive index. These parameters are crucial for the subsequent light propagation path analysis. By performing light field voxel space processing on the multi-view light field matrix data, the complex light field distribution can be converted into easy-to-calculate voxel space data. Using the resin curing material parameters to perform light propagation path analysis on these data, the propagation process of light in the resin can be described in detail. Based on this data, the light intensity value of the resin surface is calculated, and the connection between the illumination conditions and the resin curing reaction is established. Based on the obtained light curing ray tracing data, the light intensity distribution on the resin surface is further refined to form a more accurate energy distribution spectrum. Doing so can help better understand which areas have received enough energy to complete the curing process and which areas are insufficient. Combined with the properties of resin materials, using this detailed surface light intensity energy distribution data to predict resin photosensitive curing can intuitively display the expected curing level throughout the process, which is conducive to guiding the adjustment of lighting strategies in actual operations to ensure the best curing effect. Real-time photoacoustic detection technology is used to collect multi-wavelength laser feedback signal data, aiming to monitor the changes of resin materials under light in real time, extract key vector information about the characteristics of resin curing, and then determine the actual degree of curing of the material in the current state. Compared with traditional methods, this method can obtain accurate results faster, and there is no need to wait for the entire curing process to end to understand the progress. By comparing the predicted and actual curing degrees, identifying deviations in the curing process, and adjusting the light source parameters of the intelligent dental light curing machine, it is possible to achieve precise control of the light curing machine, ensure reasonable light energy distribution during the curing process, and improve the curing effect. In addition, light curing quality evaluation is performed to generate light curing quality evaluation data, which enhances the accuracy and effectiveness of dental light curing. Therefore, the real-time monitoring method of an intelligent medical dental light curing machine of the present invention irradiates the target dental light curing area with a light source, captures the light intensity information under different viewing angles, and uses the resin curing material parameters to perform light propagation path analysis on these data, so as to effectively predict the degree of curing progress of the resin under given lighting conditions, identify the deviation between the theoretical prediction and the actual situation through real-time photoacoustic detection, realize dynamic tracking and control of the resin curing process, ensure that each part can achieve the ideal curing effect, and effectively monitor and guarantee the quality of the entire curing process.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: deploying a microlens annular array on the irradiation head of the intelligent dental light curing machine, and installing a photoelectric sensor directly behind each microlens, so as to obtain annular microlens array data;

[0013] Step S12: performing microlens array angle calibration according to the annular microlens array data to generate microlens array calibration parameters;

[0014] Step S13: irradiating the target dental light-curing area with the intelligent dental light-curing machine based on the microlens array calibration parameters, and synchronously collecting multi-viewing angle light intensity signals with the photoelectric sensor to generate multi-viewing angle light intensity signal data;

[0015] Step S14: extracting light field parameters according to the microlens array calibration parameters to obtain light irradiation field parameters;

[0016] Step S15: Establishing a mapping relationship between the light intensity value of each microlens and the light direction in three-dimensional space by using the light irradiation field parameters and the multi-viewing angle light intensity signal data through the annular microlens array data, thereby constructing the multi-viewing angle light direction matrix data;

[0017] Step S16: performing light field matrix processing according to the multi-viewing angle light direction matrix data and the multi-viewing angle light intensity signal data to generate multi-viewing angle light field matrix data.

[0018] The present invention deploys a microlens annular array in the irradiation head of the intelligent dental light curing machine, and installs a photoelectric sensor directly behind each microlens, which can realize multi-angle illumination and signal collection of the target area, and improve the uniformity and coverage of the illumination. The microlens array angle calibration is performed according to the annular microlens array data to ensure that the angle and position of each microlens are in the best state, thereby ensuring the consistency and accuracy of the illumination, so that the illumination is more accurately focused on the target area, and the illumination deviation is reduced. By irradiating the calibrated microlens array and combining the real-time monitoring of the photoelectric sensor, the light intensity distribution at multiple angles can be obtained. By extracting specific light field parameters through calibration parameters, the distribution of light in the target area can be described in detail. The light intensity value is associated with the light direction to form a detailed three-dimensional light field model. By constructing multi-view light direction matrix data, the illumination effect can be more accurately controlled and predicted. A comprehensive light field matrix is ​​generated by integrating multi-view light direction matrix data and multi-view light intensity signal data. The matrix contains detailed information on light intensity and direction. This multi-view light field matrix data can fully reflect the illumination of the intelligent dental curing machine.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S21: Acquire resin curing material parameters, wherein the resin curing material parameters include resin material optical parameters and resin material light curing parameters;

[0021] Step S22: performing three-dimensional voxel grid processing on the target dental light-curing area to generate voxel grid data of the curing area;

[0022] Step S23: performing multi-view light space projection on the solidified area voxel grid data through the multi-view light field matrix data to generate light voxel mapping data;

[0023] Step S24: performing voxel light feature aggregation according to the light voxel mapping data, and performing light field voxel space processing to obtain light field voxel space data;

[0024] Step S25: performing light propagation path analysis on the light field voxel space data by using the optical parameters of the resin material to generate light-curing ray tracing data;

[0025] Step S26: performing resin surface endpoint analysis on the light-curing ray tracing data, and performing a ray endpoint neighborhood search to obtain surface area ray cluster data;

[0026] Step S27: Calculate the neighborhood average light intensity value of the surface area light cluster data to generate initial surface light intensity distribution data.

[0027] The present invention can achieve accurate description of each point in the illumination area by dividing the target area into small voxel units. This grid processing method not only improves the spatial resolution, but also facilitates the subsequent light propagation path analysis and light intensity distribution calculation. Using multi-view light field matrix data, light at different angles can be projected onto each voxel, thereby forming a detailed light-voxel mapping relationship. By aggregating the light features on each voxel, a complete light field voxel space model can be constructed. This model not only contains information on light intensity, but also includes data on light direction and path. Combined with the optical parameters of the resin material, the propagation path of light in the material can be simulated in detail. This ray tracing method can predict the specific behavior of light in the resin material, including reflection, refraction, absorption and other phenomena. By analyzing the end point of the light on the resin surface, it can be determined which light finally reaches the surface and has an impact. By calculating the neighborhood average light intensity value of each surface area light cluster, the light intensity distribution of each point on the surface can be obtained. This average light intensity value calculation method can eliminate the local fluctuations caused by a single light and provide a smoother and more stable light intensity distribution map.

[0028] Preferably, step S25 comprises the following steps:

[0029] Step S251: assigning light field characteristic parameters to light field voxel spatial data based on the optical parameters of the resin material to generate voxel optical parameter matrix data, wherein the voxel optical parameter matrix data includes voxel refractive index coefficient, voxel absorption coefficient and voxel scattering coefficient;

[0030] Step S252: performing voxel light field propagation update on the light field voxel space data through the voxel optical parameter matrix data to generate stable iterative light field data;

[0031] Step S253: identifying light source voxel units according to the stable iterative light field data, marking them as root nodes of the light propagation trajectory, and generating light source node data;

[0032] Step S254: tracing the voxel light direction of the stable iterative light field data through the light source node data to generate light propagation vector data;

[0033] Step S255: Based on the light propagation vector data, the light is propagated through each voxel unit marked as a branch node of the light trajectory by stable iteration of the light field data, thereby obtaining light path chain data;

[0034] Step S256: using the preset light propagation termination condition to identify the light propagation termination voxel of the light path chain data, thereby obtaining the photo-stereolithography ray tracing data.

[0035] The present invention constructs a detailed optical parameter matrix by assigning the optical properties of the resin material to each voxel unit. This matrix provides the refractive index, absorption coefficient and scattering coefficient of each voxel. Using the voxel optical parameter matrix, the light field information in each voxel unit can be updated to simulate the propagation process of light in the material. This iterative update method can gradually converge to a stable light field state, ensuring that the simulation results of light propagation are more accurate and reliable. By analyzing the stable iterative light field data, it is possible to determine which voxel units are the positions of the light source and mark them as the starting point or root node of the light propagation. This marking method clarifies the starting point of the light propagation. Based on the marked light source node, the propagation direction of the light in each voxel unit can be traced, and the path of the light in the material can be recorded in detail to form light propagation vector data. By tracing the light propagation vector, the path node through which the light passes can be marked in each voxel unit to form a complete light path chain, which records in detail each intermediate node of the light from the light source to the end point. By setting reasonable termination conditions for light propagation (such as light intensity falling below a certain threshold or reaching the material boundary), the end point of light propagation can be determined, the termination position of light propagation can be accurately identified, and the final photocuring ray tracing data can be generated. These tracing data not only describe the complete path of the light in the resin material, but also provide the light intensity distribution on each path.

[0036] Preferably, step S252 includes the following steps:

[0037] Step S2521: constructing a voxel interface optical interaction model for the voxel optical parameter matrix data using the Fresnel formula to generate a voxel optical interaction model;

[0038] Step S2522: constructing a voxel internal light attenuation model for the voxel optical parameter matrix data using the Beer-Lambert law to generate a voxel internal attenuation model;

[0039] Step S2523: discretizing the light propagation path according to the voxel optical interaction model and the voxel internal attenuation model, and establishing an iterative calculation model of light propagation between voxels through finite element analysis to obtain a light propagation iterative equation group;

[0040] Step S2524: performing light propagation operator calculation according to the light propagation iterative equation group to obtain light propagation operator data;

[0041] Step S2525: injecting the light field voxel space data as the initial light field, and using the light propagation operator data to perform voxel light propagation simulation to obtain voxel light propagation update data;

[0042] Step S2526: performing light field iterative updating on the light field voxel space data according to the voxel light propagation update data to generate voxel iterative light field data;

[0043] Step S2527: performing adjacent updated light field difference analysis on the voxel iterative light field data to generate iterative light field difference data, wherein the iterative light field difference data includes voxel light intensity difference data, voxel light direction difference data and light field energy difference data;

[0044] Step S2528: Based on the preset stability index threshold, the light field stability of the voxel iterative light field data is judged by iterating the light field difference data. When the difference between two adjacent iterative light fields is less than or equal to the preset stability index threshold, the light field iterative update ends and the voxel iterative light field data is marked as stable iterative light field data; otherwise, return to step S2526 for light field iterative simulation.

[0045] The present invention can describe the reflection and refraction behavior of light at different medium interfaces in detail by applying the Fresnel formula. This model can accurately simulate the interaction between the light inside the resin material and the external interface. By applying the Lambert-Beer law, the attenuation of light when propagating inside the resin material can be calculated. This model can describe the absorption and scattering process of light inside the material. By discretizing the light propagation path and combining the optical interaction model and the internal attenuation model, a complete light propagation iterative calculation model can be established. This model can describe the propagation process of light in each voxel unit in detail and form a light propagation iterative equation group. By solving the light propagation iterative equation group, light propagation operator data can be obtained, which describes the propagation characteristics of light in each voxel unit, including reflection, refraction, absorption and scattering phenomena. By injecting the initial light field data into the system and simulating it using the light propagation operator, the light field information in each voxel unit can be updated, and the light propagation process in the material can be gradually simulated. By applying the voxel light propagation update data to the light field voxel space data, the light field can be iteratively updated, and the actual light field distribution can be gradually approached to generate voxel iterative light field data. By performing a difference analysis on the light field data of two adjacent iterations, the changes after each iteration can be evaluated. By setting a stable indicator threshold and making a stability judgment based on the iterative light field difference data, it can be determined whether the light field has reached a stable state. If the difference is less than or equal to the preset threshold, it indicates that the light field has converged, and the current voxel iterative light field data can be marked as stable iterative light field data. Otherwise, it is necessary to continue the iterative simulation until a stable state is reached, ensuring that the final light field data has high accuracy and reliability.

[0046] Preferably, step S3 comprises the following steps:

[0047] Step S31: performing surface voxel energy attenuation analysis on the photocuring ray tracing data to obtain path energy attenuation ratio data;

[0048] Step S32: performing resin surface energy contribution weight processing according to the path energy attenuation ratio data, and performing voxel point cumulative light intensity energy calculation on the voxel grid data of the solidified area to generate cumulative light intensity energy contribution data;

[0049] Step S33: performing resin surface light intensity distribution processing on the initial surface light intensity distribution data by accumulating light intensity energy contribution data to generate surface light intensity energy distribution data;

[0050] Step S34: performing resin curing state parameter processing according to the resin material light curing parameters to generate curing state parameter data;

[0051] Step S35: obtaining a photosensitizer reaction rate equation, establishing a mapping relationship between the resin curing state parameter and the photosensitizer concentration according to the photosensitizer reaction rate equation and the curing state parameter data, and generating curing state parameter mapping data;

[0052] Step S36: Calculating the cumulative light dose on the resin surface based on the surface light intensity energy distribution data in a preset time window to generate cumulative light dose prediction sequence data;

[0053] Step S37: using the curing state parameter mapping data to perform a time series analysis of the photosensitivity concentration on the cumulative light dose prediction sequence data, and performing a quantification process on the resin curing degree to generate resin curing degree prediction curve data.

[0054] The present invention can determine the energy loss ratio of each light ray when it reaches the resin surface by analyzing the energy attenuation of the light during the propagation process. By considering the energy attenuation ratio of each light ray, a corresponding energy contribution weight can be assigned to each voxel point. Then, by accumulating the energy of all voxel points, the total light intensity energy contribution of each voxel point can be obtained. Using the cumulative light intensity energy contribution data, the initial surface light intensity distribution data can be further refined and corrected to form a more accurate surface light intensity energy distribution diagram, which can fully reflect the actual illumination conditions of each point on the resin surface. By analyzing the light curing parameters of the resin material, such as curing depth, curing time, etc., parameters related to the curing state can be extracted. These parameters describe the curing behavior of the resin material under different illumination conditions. By introducing the photosensitizer reaction rate equation, the resin curing state parameters can be linked to the photosensitizer concentration. By setting a time window, the cumulative light dose received by the resin surface during the time period can be calculated, and the illumination conditions of the resin surface can be dynamically tracked to form a series of cumulative light dose data. By combining the curing state parameter mapping data and the cumulative light dose prediction sequence data, the change of photosensitizer concentration over time can be analyzed and the curing degree of the resin can be quantified accordingly.

[0055] Preferably, step S4 comprises the following steps:

[0056] Step S41: A beam modulation unit is integrated in the intelligent dental light curing machine, and the beam modulation unit is located inside the handheld operating handle near the light guide output head;

[0057] Step S42: emitting multi-wavelength laser pulses to the target dental light-curing area by using a beam modulation unit through a preset set of laser excitation sequences, and performing real-time photoacoustic detection to generate multi-wavelength laser feedback signal data;

[0058] Step S43: performing photoacoustic image fusion processing on the multi-wavelength laser feedback signal data to generate real-time dynamic tomography image data;

[0059] Step S44: performing resin curing feature vector processing on the multi-parameter feature value corresponding to each pixel point in the real-time dynamic tomography image data to generate curing parameter vector space data;

[0060] Step S45: Calculate the curing degree according to the curing parameter vector space data to generate resin curing degree data;

[0061] Step S46: performing solidification parameter clustering processing on the real-time dynamic tomography image data through the resin solidification degree data, and performing solidification region boundary recognition to generate solidification region boundary data;

[0062] Step S47: marking the curing state label according to the curing area boundary data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin.

[0063] The present invention can perform multi-wavelength laser irradiation on the resin material by using a preset laser excitation sequence, so as to obtain feedback signals at different wavelengths. This multi-wavelength laser pulse emission method can fully capture the reaction of the resin material under different lighting conditions. By fusing the multi-wavelength laser feedback signal, a high-resolution photoacoustic image can be constructed. By processing the multi-parameter eigenvalues ​​of each pixel point, the eigenvector related to the resin curing can be extracted, which describes the curing state of the resin at different positions and depths, and forms a detailed curing parameter vector space. By analyzing the curing parameter vector space data, the curing degree of the resin at different positions can be quantified, and a specific curing percentage or curing index can be provided, reflecting the actual curing state of the resin under the current lighting conditions. Using the resin curing degree data, the pixels in the real-time dynamic tomography image can be clustered to distinguish different curing areas. By labeling the curing area boundary data, a corresponding curing state label (such as fully cured, partially cured, etc.) can be assigned to each area. Then, the key areas are marked from these labels, and the actual curing degree data of these areas are extracted.

[0064] Preferably, step S43 includes the following steps:

[0065] Step S431: performing photoacoustic signal preprocessing on the multi-wavelength laser feedback signal data, and performing wavelet coefficient denoising processing to obtain photoacoustic signal denoising wavelet coefficients;

[0066] Step S432: extracting the target component of the photoacoustic signal from the multi-wavelength laser feedback signal data by using the photoacoustic signal denoising wavelet coefficient to generate target photoacoustic component data;

[0067] Step S433: performing photoacoustic image reconstruction on the target photoacoustic component data based on a delay and sum algorithm to generate two-dimensional resin tomographic image sequence data;

[0068] Step S434: performing pixel point photoacoustic signal spectrum analysis on the two-dimensional resin tomographic image sequence data to generate photoacoustic spectrum characteristic diagram data;

[0069] Step S435: extracting multi-dimensional photoacoustic features of the resin according to the photoacoustic spectrum feature map data, and obtaining a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively;

[0070] Step S436: performing pixel point photoacoustic velocity estimation on the photoacoustic spectrum characteristic map data based on the two-dimensional resin tomographic image sequence data to generate a photoacoustic velocity map;

[0071] Step S437: using the photoacoustic velocity map, the photoacoustic absorption coefficient map and the photoacoustic scattering coefficient map to perform multi-parameter feature value combination on the two-dimensional resin tomographic image sequence data, and perform resin curing feature weight fusion to generate real-time dynamic tomographic image data.

[0072] The present invention can remove noise and interference in the signal through preprocessing and wavelet coefficient denoising, improve the signal-to-noise ratio, retain the effective components in the signal, and reduce the influence of background noise. By using the wavelet coefficients of the denoised photoacoustic signal, the effective signal components related to resin curing can be extracted, the target signal can be separated, irrelevant noise can be excluded, and the extracted components can be ensured to be the real reaction of the resin material under light. By applying the delay and sum algorithm, a two-dimensional resin tomographic image can be reconstructed from the target photoacoustic component data, and this algorithm can effectively convert the photoacoustic signal in the time domain into an image in the space domain. By performing spectrum analysis on the photoacoustic signal of each pixel point, the information in the frequency domain can be extracted, the distribution of different frequency components in the image can be revealed, and more physical information can be provided. By analyzing the photoacoustic spectrum characteristic map data, the photoacoustic absorption coefficient and the photoacoustic scattering coefficient can be extracted, which describes the absorption and scattering characteristics of the resin material to light and provides important optical parameters. By jointly analyzing the two-dimensional resin tomographic image sequence data and the photoacoustic spectrum characteristic map data, the photoacoustic velocity of each pixel point can be estimated, the distribution of the sound velocity inside the resin material can be provided, and the physical properties of the material are reflected. By combining the photoacoustic velocity map, the photoacoustic absorption coefficient map and the photoacoustic scattering coefficient map, the multi-parameter eigenvalue combination of the two-dimensional resin tomographic image sequence data can be performed. Then, through the weight fusion method, the importance of each parameter is comprehensively considered to provide comprehensive information on the internal structure and curing state of the resin material.

[0073] Preferably, step S5 comprises the following steps:

[0074] Step S51: aligning the time axis of the actual curing degree data of the resin based on the resin curing degree prediction curve data, and performing actual curing degree curve processing to generate actual curing degree curve data;

[0075] Step S52: using the resin curing degree prediction curve data to calculate the curing degree error of the actual curing degree curve data, and generating curing degree error curve data;

[0076] Step S53: performing graded warning of the degree of deviation on the curing degree error curve data, and adjusting the parameters through preset curing machine parameter adjustment rules to generate light source parameter adjustment values;

[0077] Step S54: the staff controls the light source parameters of the intelligent dental light curing machine through the light source parameter adjustment value to generate light source control state data;

[0078] Step S55: determining the photocuring termination state according to the light source control state data; when the photocuring is terminated, collecting the termination photoacoustic signal by using the light beam modulation unit, and performing a curing quality assessment to generate photocuring quality assessment data.

[0079] The present invention can ensure that the two are compared under the same time reference by aligning the predicted curing degree curve with the actually measured curing degree data on the time axis, eliminate the error caused by time asynchrony, and improve the accuracy of data comparison. By comparing the predicted curing degree curve and the actual curing degree curve point by point, the error value at each time point can be calculated, and the difference between the predicted value and the actual value during the curing process can be displayed in detail, providing specific error information. By analyzing the curing degree error curve data, the error can be divided into different levels, and graded warnings can be performed according to preset warning rules. At the same time, based on these error information, combined with the preset curing machine parameter adjustment rules, corresponding light source parameter adjustment values ​​can be generated. According to the generated light source parameter adjustment values, the staff can manually or automatically adjust the light source parameters of the intelligent dental light curing machine, such as light intensity, wavelength, etc., and can adjust the lighting conditions in real time to adapt to the current curing requirements, ensuring that the lighting conditions are always in the best state, thereby improving the uniformity and consistency of resin curing. By analyzing the light source control state data, it can be determined whether the light curing process has been completed. Once the curing termination is determined, the beam modulation unit is used to collect the photoacoustic signal at the termination moment for a comprehensive curing quality assessment, which significantly improves the performance of the intelligent dental light curing machine, optimizes the resin curing process, and ensures high-quality treatment results.

[0080] The present invention also provides a real-time monitoring system for an intelligent medical dental light curing machine, which executes the real-time monitoring method for an intelligent medical dental light curing machine as described above. The real-time monitoring system for an intelligent medical dental light curing machine comprises:

[0081] The multi-view light field acquisition module is used to use the intelligent dental light curing machine to irradiate the target dental light curing area with a target area light source to generate multi-view light intensity signal data; perform light field matrix processing on the multi-view light intensity signal data to generate multi-view light field matrix data;

[0082] The curing light intensity analysis module is used to obtain the parameters of the resin curing material; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; use the resin curing material parameters to perform light propagation path analysis on the light field voxel space data to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data;

[0083] The resin curing prediction module is used to process the initial surface light intensity distribution data of the resin surface through the light curing ray tracing data to generate the surface light intensity energy distribution data; and to predict the resin photosensitization curing of the surface light intensity energy distribution data using the resin curing material parameters to generate the resin curing degree prediction curve data;

[0084] The curing degree real-time monitoring module is used to perform real-time photoacoustic detection of the target dental light-curing area using the intelligent dental light-curing machine to generate multi-wavelength laser feedback signal data; perform resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extract the curing degree of key areas; and generate actual resin curing degree data;

[0085] The curing quality assessment module is used to calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data, and generate the curing degree error curve data; the light source parameters of the intelligent dental light curing machine are adjusted through the curing degree error curve data, and the light curing quality assessment is terminated to generate the light curing quality assessment data. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A schematic diagram of the steps of a real-time monitoring method for an intelligent medical dental light curing machine according to the present invention;

[0087] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0088] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0089] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0090] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0091] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0092] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0093] To achieve this, please refer to Figures 1 to 3 The present invention provides a real-time monitoring method for an intelligent medical dental light curing machine, comprising the following steps:

[0094] Step S1: using an intelligent dental light curing machine to irradiate a target dental light curing area with a target area light source to generate multi-viewing angle light intensity signal data; performing light field matrix processing on the multi-viewing angle light intensity signal data to generate multi-viewing angle light field matrix data;

[0095] Step S2: Acquire resin curing material parameters; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; perform light propagation path analysis on the light field voxel space data using the resin curing material parameters to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data;

[0096] Step S3: processing the initial surface light intensity distribution data for resin surface light intensity distribution by light-curing ray tracing data to generate surface light intensity energy distribution data; predicting resin photosensitization by using resin curing material parameters to generate resin curing degree prediction curve data;

[0097] Step S4: using the intelligent dental light curing machine to perform real-time photoacoustic detection on the target dental light curing area to generate multi-wavelength laser feedback signal data; performing resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin;

[0098] Step S5: Calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data to generate curing degree error curve data; adjust the light source parameters of the intelligent dental light curing machine using the curing degree error curve data, and perform termination light curing quality evaluation to generate light curing quality evaluation data.

[0099] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a real-time monitoring method for an intelligent medical dental light curing machine of the present invention. In this embodiment, the real-time monitoring method for an intelligent medical dental light curing machine includes the following steps:

[0100] Step S1: using an intelligent dental light curing machine to irradiate a target dental light curing area with a target area light source to generate multi-viewing angle light intensity signal data; performing light field matrix processing on the multi-viewing angle light intensity signal data to generate multi-viewing angle light field matrix data;

[0101] In an embodiment of the present invention, the intelligent dental light curing machine is equipped with multiple LED light sources, each of which can independently control the light intensity and irradiation angle. Taking the target tooth cavity filling as an example, the filling resin material is first placed in the cavity. The intelligent dental light curing machine is started, and the preset multi-view illumination program is used to control multiple LED light sources in sequence to irradiate the target cavity area. When each light source irradiates, the built-in light intensity sensor is used to collect the light intensity signal, and the light source position and angle information are recorded. For example, 5 LED light sources can be set to irradiate the cavity from five angles of 0°, 45°, 90°, 135° and 180°, respectively. Each irradiation time is 1 second, and the sensor sampling frequency is 100Hz. The collected multi-view light intensity signal data is input into the computer, and the 4D light field matrix is ​​constructed using the light field imaging principle. Specifically, the light field rendering software, such as Light Field Toolbox, can be used to map the light intensity signal data of each view to the corresponding light field matrix element, and finally generate a light field matrix data containing light intensity, angle, and position information.

[0102] Step S2: Acquire resin curing material parameters; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; perform light propagation path analysis on the light field voxel space data using the resin curing material parameters to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data;

[0103] In an embodiment of the present invention, the product manual of the resin material used is queried to obtain its curing material parameters, such as refractive index, absorption coefficient, scattering coefficient, etc. The multi-view light field matrix data generated in step S1 is imported into a computer, and the light field voxelization algorithm, such as a ray casting algorithm, is used to discretize the light field matrix into a series of voxels, each voxel containing information such as light intensity and direction, to generate light field voxel spatial data. For example, the target caries area can be divided into voxels of 1mm×1mm×1mm, and each voxel records the direction and intensity of the light passing through the voxel. Then, the Monte Carlo ray tracing algorithm is used to simulate the propagation path of light in the resin material. Specifically, according to the resin material parameters and the light field voxel spatial data, the scattering, absorption and refraction process of photons in each voxel is simulated, and the propagation path of each light ray is tracked until the light leaves the resin material or is completely absorbed. The light propagation path information is recorded to generate light-curing ray tracing data. Finally, according to the light-curing ray tracing data, the incident light intensity value of each point on the resin surface is calculated to generate initial surface light intensity distribution data. For example, the number of photons received by each surface voxel can be counted and converted into a light intensity value.

[0104] Step S3: processing the initial surface light intensity distribution data for resin surface light intensity distribution by light-curing ray tracing data to generate surface light intensity energy distribution data; predicting resin photosensitization by using resin curing material parameters to generate resin curing degree prediction curve data;

[0105] In the embodiment of the present invention, the light-curing ray tracing data generated in step S2 is used to analyze the reflection, refraction and scattering of light on the resin surface. For example, the reflectivity and transmittance of light on the resin surface can be calculated according to the Fresnel formula, and the scattering intensity of light on the resin surface can be calculated according to the scattering coefficient. According to the light propagation path and the surface optical characteristics, the initial surface light intensity distribution data is corrected, for example, the light intensity of the reflected light is subtracted from the surface light intensity, and the light intensity of the scattered light is redistributed to the surrounding surface voxels, and finally the surface light intensity energy distribution data is generated. For example, optical simulation software, such as TracePro, can be used to simulate the propagation process of light on the resin surface and calculate the light intensity distribution of each surface voxel. Then, according to the photosensitive curing characteristics of the resin material, such as the relationship curve between the curing depth and the light intensity energy, the surface light intensity energy distribution data is used to predict the curing degree of the resin at different depths, and the resin curing degree prediction curve data is generated. For example, the surface light intensity energy distribution data can be input into the resin curing model, the curing degree at each depth is calculated, and a curve of the curing degree changing with the depth is plotted.

[0106] Step S4: using the intelligent dental light curing machine to perform real-time photoacoustic detection on the target dental light curing area to generate multi-wavelength laser feedback signal data; performing resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin;

[0107] In an embodiment of the present invention, the intelligent dental light curing machine is equipped with a multi-wavelength laser transmitter and a photoacoustic signal receiver. During the light curing process, a multi-wavelength laser transmitter is used to, for example, emit lasers of two wavelengths of 635nm and 850nm, to scan and irradiate the target caries area. At the same time, the photoacoustic signal receiver is used to collect the photoacoustic signal generated by the resin material. The intensity of the photoacoustic signal is related to the degree of curing of the resin material. The collected multi-wavelength laser feedback signal data is input into a computer, and the resin curing feature vector is extracted using methods such as principal component analysis, such as the intensity ratio of photoacoustic signals of different wavelengths, the frequency characteristics of the photoacoustic signal, etc. According to a pre-established relationship model between the resin curing degree and the feature vector, such as a neural network model, the resin curing degree at different positions in the target caries area is calculated, and the curing degree of key areas, such as the bottom and edge of the caries, is extracted to generate the actual curing degree data of the resin.

[0108] Step S5: Calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data to generate curing degree error curve data; adjust the light source parameters of the intelligent dental light curing machine using the curing degree error curve data, and perform termination light curing quality evaluation to generate light curing quality evaluation data.

[0109] In an embodiment of the present invention, the resin curing degree prediction curve data generated in step S3 and the resin actual curing degree data generated in step S4 are imported into a computer. The difference between the predicted curing degree and the actual curing degree at each depth is calculated to generate curing degree error curve data. For example, the predicted curing degree curve and the actual curing degree curve can be plotted on the same graph, and the distance between the two curves is calculated. According to the curing degree error curve data, it is judged whether there is insufficient curing or over-curing during the photocuring process. For example, if the curing degree error at a certain depth exceeds a preset threshold, it is considered that there is insufficient curing or over-curing at the depth. According to the error analysis results, the light source parameters of the intelligent dental light curing machine are adjusted in real time. For example, if it is found that there is insufficient curing at the bottom of the caries, the irradiation time or intensity of the bottom light source can be increased; if it is found that there is over-curing at the edge of the caries, the irradiation time or intensity of the edge light source can be reduced. After the photocuring is completed, real-time photoacoustic detection is performed again, and the photocuring quality is evaluated according to the actual curing degree data of the resin, such as calculating the average curing degree, curing uniformity and other indicators, and generating photocuring quality evaluation data. For example, the final resin cure profile can be compared with the ideal cure profile to evaluate the light-curing effect and generate a light-curing quality report.

[0110] Preferably, step S1 comprises the following steps:

[0111] Step S11: deploying a microlens annular array on the irradiation head of the intelligent dental light curing machine, and installing a photoelectric sensor directly behind each microlens, so as to obtain annular microlens array data;

[0112] Step S12: performing microlens array angle calibration according to the annular microlens array data to generate microlens array calibration parameters;

[0113] Step S13: irradiating the target dental light-curing area with the intelligent dental light-curing machine based on the microlens array calibration parameters, and synchronously collecting multi-viewing angle light intensity signals with the photoelectric sensor to generate multi-viewing angle light intensity signal data;

[0114] Step S14: extracting light field parameters according to the microlens array calibration parameters to obtain light irradiation field parameters;

[0115] Step S15: Establishing a mapping relationship between the light intensity value of each microlens and the light direction in three-dimensional space by using the light irradiation field parameters and the multi-viewing angle light intensity signal data through the annular microlens array data, thereby constructing the multi-viewing angle light direction matrix data;

[0116] Step S16: performing light field matrix processing according to the multi-viewing angle light direction matrix data and the multi-viewing angle light intensity signal data to generate multi-viewing angle light field matrix data.

[0117] In an embodiment of the present invention, the irradiation head of the intelligent dental light curing machine is disassembled. A microlens annular array of appropriate size is selected, for example, an annular array consisting of 100 microlenses with a diameter of 1 mm. The microlens annular array is fixed in front of the light source of the irradiation head, ensuring that the central axis of each microlens is parallel to the central axis of the irradiation head. A photoelectric sensor, such as a photodiode, is installed directly behind each microlens to receive light passing through the microlens. The irradiation head is reinstalled on the intelligent dental light curing machine. By fitting the center position of each microlens imaging, the direction vector of the optical axis of the microlens in the irradiation head coordinate system is obtained. Then, a precision goniometer is used to measure the direction vector of the central axis of the irradiation head in the external reference coordinate system. The optical axis direction vector of each microlens is converted to the external reference coordinate system and compared with the direction vector of the central axis of the irradiation head, and the angle between the optical axis of each microlens and the central axis of the irradiation head is calculated, that is, the viewing angle of the microlens. The light source of the intelligent dental light curing machine is controlled to irradiate the target dental light curing area. For example, the target area can be divided into several layers, and the irradiation time of each layer is 10 seconds. While the light source is irradiating, the photoelectric sensor installed in step S11 is used to synchronously collect the light intensity signal received by each microlens. For example, a data acquisition card can be used to record the output voltage value of each photoelectric sensor at a sampling frequency of 1kHz. According to the microlens array calibration parameters generated in step S12, the light irradiation range corresponding to each microlens is calculated. For example, the projection range of the microlens on the target area, that is, the light irradiation field corresponding to the microlens can be calculated according to the focal length and viewing angle of the microlens. For example, a ray tracing algorithm can be used to simulate the propagation path of the light after passing through the microlens, and the direction vector of the light received by each microlens can be calculated. Then, the light intensity value of each microlens is associated with its corresponding light direction vector to construct multi-view light direction matrix data. For example, the target area can be discretized into a series of voxels, each voxel corresponds to a light direction vector, and the light intensity value of each microlens is assigned to the corresponding voxel. According to the viewing angle information of each microlens, the multi-view light intensity signal data is mapped to the corresponding three-dimensional space position to generate multi-view light field matrix data. For example, the light intensity signal value of each microlens can be multiplied by the corresponding light direction vector, and the result can be accumulated on the corresponding voxel to finally obtain a light field matrix data containing light intensity, angle, and position information.

[0118] Preferably, step S2 comprises the following steps:

[0119] Step S21: Acquire resin curing material parameters, wherein the resin curing material parameters include resin material optical parameters and resin material light curing parameters;

[0120] Step S22: performing three-dimensional voxel grid processing on the target dental light-curing area to generate voxel grid data of the curing area;

[0121] Step S23: performing multi-view light space projection on the solidified area voxel grid data through the multi-view light field matrix data to generate light voxel mapping data;

[0122] Step S24: performing voxel light feature aggregation according to the light voxel mapping data, and performing light field voxel space processing to obtain light field voxel space data;

[0123] Step S25: performing light propagation path analysis on the light field voxel space data by using the optical parameters of the resin material to generate light-curing ray tracing data;

[0124] Step S26: performing resin surface endpoint analysis on the light-curing ray tracing data, and performing a ray endpoint neighborhood search to obtain surface area ray cluster data;

[0125] Step S27: Calculate the neighborhood average light intensity value of the surface area light cluster data to generate initial surface light intensity distribution data.

[0126] In an embodiment of the present invention, the product manual or relevant literature of the resin material used is queried to obtain its optical parameters and light curing parameters. The optical parameters of the resin material include refractive index, absorption coefficient, scattering coefficient, etc., which describe the propagation characteristics of light in the resin material. The light curing parameters of the resin material include the relationship curve between the curing depth and the light intensity energy, the relationship curve between the curing time and the light intensity, etc., which describe the curing characteristics of the resin material under light. The three-dimensional model data of the target dental light curing area is obtained by using a three-dimensional scanner or a computer tomography (CT) device. For example, an intraoral scanner can be used to scan the caries area to obtain its three-dimensional point cloud data. The three-dimensional model data is imported into a computer, and the target area is voxelized using a three-dimensional modeling software, such as MeshLab, that is, the target area is discretized into a series of regularly arranged cubic voxels. The multi-view light field matrix data generated in step S16 and the curing area voxel grid data generated in step S22 are imported into the computer. According to the position coordinates of each voxel, the projection position of the voxel in the multi-view light field matrix is ​​calculated. For example, a ray tracing algorithm can be used to simulate light rays emitted from the center of each voxel, passing through the light field matrix until it reaches the light source, and record the coordinates of the penetration point of the light rays on the light field matrix. The projection position of each voxel is associated with the corresponding light field matrix element value to generate light voxel mapping data. The intensity values ​​of multiple light rays passing through the same voxel are averaged to obtain the average light intensity value of the voxel. The direction vectors of multiple light rays passing through the same voxel can also be calculated, and statistical analysis can be performed, such as calculating the average and standard deviation of the light direction to obtain the light direction distribution characteristics of the voxel. The Monte Carlo ray tracing algorithm is used to simulate the propagation process of light in the resin material. Specifically, according to the refractive index, absorption coefficient and scattering coefficient of the resin material, the scattering, absorption and refraction process of photons in each voxel is simulated, and the propagation path of each light ray is tracked until the light leaves the resin material or is completely absorbed. The light propagation path information is recorded to generate light curing ray tracing data. All light paths can be traversed to determine whether the end point of the light is located in the voxel on the resin surface. For each light endpoint, a neighborhood search is performed around it, for example, a spherical area with a radius of 0.5 mm is searched to find all the light whose endpoints are within the area, and the surface area light cluster data is generated. The intensity values ​​of all the light in each light cluster are averaged to obtain the average light intensity value of the neighborhood of the light endpoint. The coordinates of each light endpoint and its corresponding average light intensity value are associated to generate the initial surface light intensity distribution data.

[0127] Preferably, step S25 comprises the following steps:

[0128] Step S251: assigning light field characteristic parameters to light field voxel spatial data based on the optical parameters of the resin material to generate voxel optical parameter matrix data, wherein the voxel optical parameter matrix data includes voxel refractive index coefficient, voxel absorption coefficient and voxel scattering coefficient;

[0129] Step S252: performing voxel light field propagation update on the light field voxel space data through the voxel optical parameter matrix data to generate stable iterative light field data;

[0130] Step S253: identifying light source voxel units according to the stable iterative light field data, marking them as root nodes of the light propagation trajectory, and generating light source node data;

[0131] Step S254: tracing the voxel light direction of the stable iterative light field data through the light source node data to generate light propagation vector data;

[0132] Step S255: Based on the light propagation vector data, the light is propagated through each voxel unit marked as a branch node of the light trajectory by stable iteration of the light field data, thereby obtaining light path chain data;

[0133] Step S256: using the preset light propagation termination condition to identify the light propagation termination voxel of the light path chain data, thereby obtaining the photo-stereolithography ray tracing data.

[0134] In the embodiment of the present invention, the corresponding optical parameters are assigned to each voxel according to its material properties, such as resin type, curing state, etc. For example, the refractive index of the uncured resin voxel can be set to 1.5, and the absorption coefficient can be set to 0.1 mm -1 , the scattering coefficient is set to 1mm -1 ; Set the refractive index of the cured resin voxel to 1.52 and the absorption coefficient to 0.05 mm -1 , the scattering coefficient is set to 0.8mm -1, the optical parameters of each voxel are stored in the corresponding voxel, and the voxel optical parameter matrix data is generated. The propagation process of light in the voxel space is simulated by numerical methods such as the finite difference method or the finite element method. Specifically, the light field change of each voxel can be calculated according to the optical parameters of each voxel and the light field information of the surrounding voxels, and the light field value of the voxel can be updated. For example, the finite difference format of the Helmholtz equation can be used to calculate the light intensity and phase change of each voxel. Repeat the iterative calculation until the light field distribution reaches a stable state, that is, the change in the light field value is less than a preset threshold, such as 0.01%, to generate stable iterative light field data. According to the geometric shape and position information of the light source, the projection area of ​​the light source in the voxel space is determined, and all voxels in the area are marked as light source voxel units. The light source voxel unit is marked as the root node of the light propagation trajectory, and the light source node data is generated. The Monte Carlo method or the ray casting algorithm is used to simulate the process of light emitting from the light source voxel unit and propagating in the voxel space. Specifically, the propagation direction and intensity change of the light in each voxel can be calculated according to the optical parameters of each voxel and the direction of the light, and the direction and intensity value of the light can be updated. For example, the refraction direction of the light at the voxel interface can be calculated according to Snell's law, and the absorption attenuation of the light in the voxel can be calculated according to the Beer-Lambert law to generate light propagation vector data. For each light path, each voxel unit it passes through is marked as a branch node of the light trajectory. For example, the coordinates of each voxel on the light path can be stored in a linked list, and the linked list is used as the branch node information of the light trajectory. The light propagation termination condition is preset, such as the light propagation distance exceeds the preset threshold, the light intensity is attenuated below the preset threshold, or the light propagates to the model boundary. Then, the light path chain data generated in step S255 is analyzed using the preset light propagation termination condition, the light propagation path that meets the termination condition is identified, and it is marked as a terminated state. Finally, the photocuring ray tracing data containing all valid light propagation paths and energy attenuation information is obtained.

[0135] Preferably, step S252 includes the following steps:

[0136] Step S2521: constructing a voxel interface optical interaction model for the voxel optical parameter matrix data using the Fresnel formula to generate a voxel optical interaction model;

[0137] Step S2522: constructing a voxel internal light attenuation model for the voxel optical parameter matrix data using the Beer-Lambert law to generate a voxel internal attenuation model;

[0138] Step S2523: discretizing the light propagation path according to the voxel optical interaction model and the voxel internal attenuation model, and establishing an iterative calculation model of light propagation between voxels through finite element analysis to obtain a light propagation iterative equation group;

[0139] Step S2524: performing light propagation operator calculation according to the light propagation iterative equation group to obtain light propagation operator data;

[0140] Step S2525: injecting the light field voxel space data as the initial light field, and using the light propagation operator data to perform voxel light propagation simulation to obtain voxel light propagation update data;

[0141] Step S2526: performing light field iterative updating on the light field voxel space data according to the voxel light propagation update data to generate voxel iterative light field data;

[0142] Step S2527: performing adjacent updated light field difference analysis on the voxel iterative light field data to generate iterative light field difference data, wherein the iterative light field difference data includes voxel light intensity difference data, voxel light direction difference data and light field energy difference data;

[0143] Step S2528: Based on the preset stability index threshold, the light field stability of the voxel iterative light field data is judged by iterating the light field difference data. When the difference between two adjacent iterative light fields is less than or equal to the preset stability index threshold, the light field iterative update ends and the voxel iterative light field data is marked as stable iterative light field data; otherwise, return to step S2526 for light field iterative simulation.

[0144] In the embodiment of the present invention, the Fresnel formula is used to calculate the reflection and refraction coefficients of light at the voxel interface. The Fresnel formula describes the reflection and refraction of light at the interface of two media, and its expression is: Rs = |n1×cosθi―n2×cosθt|÷|n1×cosθi+n2×cosθt| 2 ; Rp=|n1×cosθt―n2×cosθi|÷|n1×cosθt+n2×cosθi| 2; Where Rs and Rp represent the reflectivity of s-polarized light and p-polarized light respectively, n1 and n2 represent the refractive index of the two media respectively, θi represents the angle of incidence, and θt represents the angle of refraction. For example, when light is incident from a voxel with a refractive index of 1.5 to a voxel with a refractive index of 1.52, and the angle of incidence is 30°, the reflectivity of s-polarized light and p-polarized light can be calculated to be 0.0012 and 0.0004 respectively according to the Fresnel formula. According to the absorption coefficient and scattering coefficient of each voxel, the Lambert-Beer law is used to calculate the energy attenuation of light when it passes through the voxel. Its expression is: I=I0×exp(―μ×d); where I represents the intensity of light after propagating a distance d, I0 represents the initial intensity of light, and μ represents the absorption coefficient of the medium. For example, when light propagates for 1 mm in a voxel with an absorption coefficient of 0.1 mm-1, the intensity of light can be calculated to be attenuated to 90.5% of the initial intensity according to the Lambert-Beer law. Each voxel is divided into several sub-voxels, and the propagation path of light in each sub-voxel is regarded as a straight line segment. Then, the finite element analysis method is used to establish an iterative calculation model for the propagation of light between voxels. For example, the light field value of each sub-voxel can be taken as an unknown quantity, and the optical interactions such as reflection, refraction and absorption of light at the sub-voxel interface can be expressed in the form of a linear equation system. Finally, the iterative equation system for light propagation is obtained, for example: A×X=b; wherein A represents the coefficient matrix, X represents the light intensity vector of each node to be solved, and b represents the constant vector. The elements of the coefficient matrix A are determined by information such as voxel optical parameters, voxel geometry and light propagation direction, and the constant vector b is determined by the light source information. According to the iterative equation system for light propagation obtained in step S2523, the light propagation operator is calculated. For example, the coefficient matrix A can be subjected to LU decomposition or Gaussian elimination operations to obtain light propagation operator data, for example: X=A ―1 ×b; where A - 1 represents the inverse matrix of the coefficient matrix A. The light propagation operator data describes the law of light propagation in the voxel space. Substitute the initial light field vector X into the light propagation iterative equation group to calculate the light field vector X' at the next moment. Use X' as the new initial light field vector and repeat the iterative calculation until the light field distribution reaches a stable state. The light field data obtained by each iteration is weighted averaged with the light field data of the previous iteration to obtain new light field data. For example, the following formula can be used to iteratively update the light field: X new =α×X old +(1―α)×X'; where X new represents the new light field data, X oldrepresents the light field data of the last iteration, X' represents the light field data calculated in this iteration, α represents the weighting coefficient, and the value range is 0 to 1, and voxel iterative light field data is generated. The light intensity difference, light direction difference and light field energy difference of each voxel between two adjacent iterative light fields are calculated. For example, the voxel light intensity difference can be calculated by the following formula: ΔI = |Inew-Iold|; wherein ΔI represents the voxel light intensity difference, Inew represents the light intensity value of the voxel in the new light field data, and Iold represents the light intensity value of the voxel in the light field data of the last iteration, and iterative light field difference data is generated, including voxel light intensity difference data, voxel light direction difference data and light field energy difference data. It is determined whether the difference of each voxel between two adjacent iterative light fields is less than or equal to the corresponding threshold. If the difference of all voxels is less than or equal to the corresponding threshold, it is considered that the light field iterative update is completed, and the voxel iterative light field data is marked as stable iterative light field data; otherwise, return to step S2526 to perform light field iterative simulation until the light field reaches a stable state.

[0145] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S3 in the flowchart, in this example, step S3 includes:

[0146] Step S31: performing surface voxel energy attenuation analysis on the photocuring ray tracing data to obtain path energy attenuation ratio data;

[0147] In the embodiment of the present invention, the energy attenuation of the light in each voxel is calculated according to the Lambert-Beer law, and the energy of each light when it reaches the resin surface is compared with its initial energy to obtain the path energy attenuation ratio data. For example, if the initial energy of a light is 1, and the energy when it reaches the resin surface after passing through a series of voxels is 0.5, then the path energy attenuation ratio of the light is 0.5.

[0148] Step S32: performing resin surface energy contribution weight processing according to the path energy attenuation ratio data, and performing voxel point cumulative light intensity energy calculation on the voxel grid data of the solidified area to generate cumulative light intensity energy contribution data;

[0149] In the embodiment of the present invention, a light with a larger path energy attenuation ratio is assigned a smaller weight, and a light with a smaller path energy attenuation ratio is assigned a larger weight. For example, the light weight can be calculated using the following formula: w = 1-β × (1-E ÷ E0); wherein w represents the light weight, β represents the weight adjustment coefficient, ranging from 0 to 1, E represents the energy of the light when it reaches the resin surface, and E0 represents the initial energy of the light. The energy of all the light received by each voxel surface is multiplied by the corresponding weight, and then accumulated to obtain the cumulative light intensity energy contribution data of the voxel.

[0150] Step S33: performing resin surface light intensity distribution processing on the initial surface light intensity distribution data by accumulating light intensity energy contribution data to generate surface light intensity energy distribution data;

[0151] In the embodiment of the present invention, the initial light intensity value of each voxel can be multiplied by the corresponding cumulative light intensity energy contribution value to obtain more accurate surface light intensity energy distribution data, which reflects the actual light energy distribution received by each position on the resin surface.

[0152] Step S34: performing resin curing state parameter processing according to the resin material light curing parameters to generate curing state parameter data;

[0153] In the embodiment of the present invention, the resin curing state parameters are processed according to the resin material photocuring parameters obtained in step S21, such as the curing depth of the resin, the photocuring kinetic model parameters, etc. For example, the curing state parameters at different depths below the resin surface, such as the curing degree, the polymerization rate, etc., can be calculated according to the curing depth of the resin, and these parameters can be calculated using the photocuring kinetic model of the resin. The surface light intensity energy distribution data can also be converted into resin curing time data according to the relationship curve between the curing time and the light intensity of the resin material.

[0154] Step S35: obtaining a photosensitizer reaction rate equation, establishing a mapping relationship between the resin curing state parameter and the photosensitizer concentration according to the photosensitizer reaction rate equation and the curing state parameter data, and generating curing state parameter mapping data;

[0155] In an embodiment of the present invention, a photosensitizer reaction rate equation is obtained, which describes the change pattern of photosensitizer concentration over time and light intensity. For example, the following form of photosensitizer reaction rate equation can be used: dC / dt=-k×I×C; wherein C represents the photosensitizer concentration, t represents the time, k represents the reaction rate constant, and I represents the light intensity. According to the photosensitizer reaction rate equation and the curing state parameter data generated in step S34, a mapping relationship between the resin curing state parameter and the photosensitizer concentration is established. For example, the resin curing depth or the resin curing time can be associated with the photosensitizer concentration according to the curing mechanism of the resin material. For example, the resin curing depth can be set to be proportional to the photosensitizer concentration, or the resin curing time can be set to be inversely proportional to the photosensitizer concentration to generate curing state parameter mapping data.

[0156] Step S36: Calculating the cumulative light dose on the resin surface based on the surface light intensity energy distribution data in a preset time window to generate cumulative light dose prediction sequence data;

[0157] In an embodiment of the present invention, based on a preset time window, for example, once every 0.1 seconds, the surface light intensity energy distribution data generated in step S33 is integrated, the cumulative light dose at each position on the resin surface at each time point is calculated, and the cumulative light dose prediction sequence data is generated.

[0158] Step S37: using the curing state parameter mapping data to perform a time series analysis of the photosensitivity concentration on the cumulative light dose prediction sequence data, and performing a quantification process on the resin curing degree to generate resin curing degree prediction curve data.

[0159] In an embodiment of the present invention, the residual photosensitizer concentration at the corresponding time point is calculated using the photosensitizer reaction rate equation according to the cumulative light dose at each time point. Then, the corresponding curing state parameter, such as the degree of curing, is searched according to the residual photosensitizer concentration and the curing state parameter mapping data. For example, the photosensitizer concentration change of each voxel surface in each time window can be calculated based on the cumulative light dose prediction sequence data, and the photosensitizer concentration time series data of the voxel can be obtained by adding them up. Then, according to the curing mechanism of the resin material, the photosensitizer concentration time series data is converted into resin curing degree data. Finally, the curing degree at each time point is connected to generate the resin curing degree prediction curve data.

[0160] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0161] Step S41: A beam modulation unit is integrated in the intelligent dental light curing machine, and the beam modulation unit is located inside the handheld operating handle near the light guide output head;

[0162] In an embodiment of the present invention, the handheld operating handle of the intelligent dental light curing machine is disassembled, and a space is reserved near the optical waveguide output head for installing a beam modulation unit. A suitable beam modulation unit is selected, and the beam modulation unit includes multiple lasers, an adjustable optical attenuator, a beam shaper, and a beam synthesizer, which are used to generate multi-wavelength laser pulses that meet the requirements of the optimized excitation sequence, and can accurately control the energy density, pulse width, and spatial distribution of the laser pulses, and fix them in the reserved space. The beam modulation unit is used to control parameters such as the wavelength, pulse width, and repetition frequency of the laser. The handheld operating handle is reassembled to ensure that the beam modulation unit is well connected to the optical waveguide output head. For example, an optical fiber can be used to connect the output end of the beam modulation unit to the input end of the optical waveguide output head.

[0163] Step S42: emitting multi-wavelength laser pulses to the target dental light-curing area by using a beam modulation unit through a preset set of laser excitation sequences, and performing real-time photoacoustic detection to generate multi-wavelength laser feedback signal data;

[0164] In an embodiment of the present invention, a group of laser excitation sequences are preset, such as laser pulse sequences including different wavelengths, different pulse widths and different repetition frequencies. For example, a group of laser pulse sequences including two wavelengths of 635nm and 850nm can be set, and the laser pulse width of each wavelength is 10ns and the repetition frequency is 10kHz. Using the beam modulation unit installed in step S41, multi-wavelength laser pulses are emitted to the target dental light-curing area according to the preset laser excitation sequence. At the same time, a built-in photoacoustic detector of the intelligent dental light-curing machine, such as a piezoelectric sensor, is used to perform real-time photoacoustic detection of the target area and collect photoacoustic signals. The photoacoustic signal data collected after each laser pulse is emitted is associated with the corresponding laser parameter information to generate multi-wavelength laser feedback signal data.

[0165] Step S43: performing photoacoustic image fusion processing on the multi-wavelength laser feedback signal data to generate real-time dynamic tomography image data;

[0166] In an embodiment of the present invention, the multi-wavelength laser feedback signal data generated in step S42 is subjected to photoacoustic image fusion processing. For example, the photoacoustic signal data of different wavelengths can be reconstructed into corresponding photoacoustic images using a time-delay superposition algorithm or a back-projection algorithm, and the photoacoustic images of different wavelengths can be fused to generate real-time dynamic tomographic image data. For example, the photoacoustic image generated by 635nm laser excitation and the photoacoustic image generated by 850nm laser excitation can be superimposed to generate a fused photoacoustic image. The real-time dynamic tomographic image data reflects the changes in the light absorption characteristics of the target area over time.

[0167] Step S44: performing resin curing feature vector processing on the multi-parameter feature value corresponding to each pixel point in the real-time dynamic tomography image data to generate curing parameter vector space data;

[0168] In an embodiment of the present invention, the real-time dynamic tomographic image data generated in step S43 is imported into a computer to extract the multi-parameter characteristic values ​​corresponding to each pixel, such as the photoacoustic signal amplitude, the photoacoustic signal frequency, the photoacoustic signal attenuation coefficient, etc. The multi-parameter characteristic values ​​of each pixel are combined into a vector to generate solidification parameter vector space data. For example, the 635nm photoacoustic signal amplitude, the 850nm photoacoustic signal amplitude and the photoacoustic signal attenuation coefficient of each pixel can be combined into a three-dimensional vector.

[0169] Step S45: Calculate the curing degree according to the curing parameter vector space data to generate resin curing degree data;

[0170] In an embodiment of the present invention, the curing degree is calculated according to the curing parameter vector space data generated in step S44. For example, a machine learning algorithm, such as a support vector machine or a neural network, can be used to establish a relationship model between the curing parameter vector and the resin curing degree, and the resin curing degree of each pixel can be predicted using the model. For example, a trained support vector machine model can be used to input the curing parameter vector of each pixel into the model and output the corresponding resin curing degree value. The resin curing degree value of each pixel is stored to generate resin curing degree data.

[0171] Step S46: performing solidification parameter clustering processing on the real-time dynamic tomography image data through the resin solidification degree data, and performing solidification region boundary recognition to generate solidification region boundary data;

[0172] In the embodiment of the present invention, the curing parameter clustering process is performed on the real-time dynamic tomography image data generated in step S43 by using the resin curing degree data generated in step S45. For example, the pixels with similar curing degrees can be clustered together using a K-means algorithm or a hierarchical clustering algorithm. Then, the clustering results are subjected to boundary recognition, for example, using an edge detection algorithm or a region growing algorithm, to identify the boundaries between regions with different curing degrees, and generate curing region boundary data.

[0173] Step S47: marking the curing state label according to the curing area boundary data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin.

[0174] In the embodiment of the present invention, the real-time dynamic tomography image data is labeled with a solidification state label according to the solidification area boundary data generated in step S46. For example, the pixels belonging to the same solidification degree area can be labeled with the same label, for example, the area with a higher degree of solidification is labeled as "solidified", and the area with a lower degree of solidification is labeled as "unsolidified". Then, according to the pre-set key areas, such as the bottom or edge of the caries cavity, the solidification degree value of the key area is extracted to generate the actual curing degree data of the resin. For example, the average curing degree value of all pixels in the key area can be calculated as the actual curing degree of the resin in the area.

[0175] Preferably, step S43 includes the following steps:

[0176] Step S431: performing photoacoustic signal preprocessing on the multi-wavelength laser feedback signal data, and performing wavelet coefficient denoising processing to obtain photoacoustic signal denoising wavelet coefficients;

[0177] Step S432: extracting the target component of the photoacoustic signal from the multi-wavelength laser feedback signal data by using the photoacoustic signal denoising wavelet coefficient to generate target photoacoustic component data;

[0178] Step S433: performing photoacoustic image reconstruction on the target photoacoustic component data based on a delay and sum algorithm to generate two-dimensional resin tomographic image sequence data;

[0179] Step S434: performing pixel point photoacoustic signal spectrum analysis on the two-dimensional resin tomographic image sequence data to generate photoacoustic spectrum characteristic diagram data;

[0180] Step S435: extracting multi-dimensional photoacoustic features of the resin according to the photoacoustic spectrum feature map data, and obtaining a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively;

[0181] Step S436: performing pixel point photoacoustic velocity estimation on the photoacoustic spectrum characteristic map data based on the two-dimensional resin tomographic image sequence data to generate a photoacoustic velocity map;

[0182] Step S437: using the photoacoustic velocity map, the photoacoustic absorption coefficient map and the photoacoustic scattering coefficient map to perform multi-parameter feature value combination on the two-dimensional resin tomographic image sequence data, and perform resin curing feature weight fusion to generate real-time dynamic tomographic image data.

[0183] In an embodiment of the present invention, a bandpass filter is used to limit the frequency range of the photoacoustic signal to between 1MHz and 10MHz. Then, the pre-processed photoacoustic signal is subjected to wavelet coefficient denoising. For example, the photoacoustic signal can be decomposed into sub-bands of different frequencies using wavelet transform, and each sub-band is subjected to threshold denoising to remove the noise wavelet coefficients and retain the signal wavelet coefficients. The denoised wavelet coefficients are reconstructed to obtain the denoised wavelet coefficients of the photoacoustic signal. According to the photoacoustic properties of the resin material, the wavelet coefficients in a specific frequency range are selected as the target photoacoustic component. For example, the wavelet coefficients in the frequency range of 2MHz to 5MHz can be selected as the target component of the photoacoustic signal of the resin material. The wavelet coefficients of the target photoacoustic component are reconstructed to generate the target photoacoustic component data. The target photoacoustic component data received by each photoacoustic detector are delayed and superimposed according to the propagation time of the photoacoustic signal and the spatial position of the photoacoustic detector to obtain the photoacoustic intensity distribution image of the target area. The photoacoustic intensity distribution images at different times are combined to generate two-dimensional resin tomographic image sequence data. The two-dimensional resin tomographic image sequence data reflects the change of the light absorption characteristics of the target area over time. Perform Fourier transform on the photoacoustic signal of each pixel to obtain the photoacoustic signal spectrum of the pixel. Draw the photoacoustic signal spectrum of each pixel in the form of an image to generate photoacoustic spectrum feature map data. The photoacoustic spectrum feature map data reflects the distribution of the light absorption characteristics of the target area at different frequencies. According to the photoacoustic spectrum feature map data, calculate the photoacoustic absorption coefficient and photoacoustic scattering coefficient of each pixel. For example, the photoacoustic diffusion equation can be used to associate the photoacoustic spectrum feature map data with the photoacoustic absorption coefficient and the photoacoustic scattering coefficient, and the photoacoustic absorption coefficient and the photoacoustic scattering coefficient are solved by the inversion algorithm. The photoacoustic absorption coefficient and the photoacoustic scattering coefficient of each pixel are stored to generate a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively. The photoacoustic velocity of each pixel is calculated using the flight time of the photoacoustic signal and the spatial position of the photoacoustic detector. For example, the cross-correlation algorithm can be used to calculate the time delay between the photoacoustic signals received by two adjacent photoacoustic detectors, and the photoacoustic velocity is calculated based on the time delay and the distance between the photoacoustic detectors. The photoacoustic velocity, photoacoustic absorption coefficient and photoacoustic scattering coefficient of each pixel are combined into a vector as the multi-parameter eigenvalue of the pixel. Then, the multi-parameter eigenvalues ​​are fused with resin curing feature weights. For example, different weights can be assigned to different eigenvalues ​​according to the photocuring characteristics of the resin material, and the weighted eigenvalues ​​can be fused to generate real-time dynamic tomographic image data. For example, the photoacoustic absorption coefficient can be assigned a larger weight, the photoacoustic scattering coefficient can be assigned a smaller weight, and the weighted photoacoustic absorption coefficient and photoacoustic scattering coefficient can be linearly combined to generate real-time dynamic tomographic image data.

[0184] Preferably, step S5 comprises the following steps:

[0185] Step S51: aligning the time axis of the actual curing degree data of the resin based on the resin curing degree prediction curve data, and performing actual curing degree curve processing to generate actual curing degree curve data;

[0186] Step S52: using the resin curing degree prediction curve data to calculate the curing degree error of the actual curing degree curve data, and generating curing degree error curve data;

[0187] Step S53: performing graded warning of the degree of deviation on the curing degree error curve data, and adjusting the parameters through preset curing machine parameter adjustment rules to generate light source parameter adjustment values;

[0188] Step S54: the staff controls the light source parameters of the intelligent dental light curing machine through the light source parameter adjustment value to generate light source control state data;

[0189] Step S55: determining the photocuring termination state according to the light source control state data; when the photocuring is terminated, collecting the termination photoacoustic signal by using the light beam modulation unit, and performing a curing quality assessment to generate photocuring quality assessment data.

[0190] In an embodiment of the present invention, according to the time point when the actual curing degree data of the resin begins to rise, the time axis of the predicted curing degree curve is shifted to align with the time axis of the actual curing degree curve. For example, if the actual curing degree data begins to rise at the 5th second, the time axis of the predicted curing degree curve is shifted to the right for 5 seconds. Then, the actual curing degree data after the time axis alignment is subjected to curve smoothing, such as using a moving average filter or a Savitzky-Golay filter, etc., to remove noise and fluctuations and generate actual curing degree curve data. The difference between the predicted curing degree value and the actual curing degree value corresponding to each time point is calculated as the curing degree error at the time point. For example, the curing degree error can be divided into different levels, such as slight deviation, medium deviation and severe deviation, etc., according to a pre-set error threshold. For example, an error less than 5% can be divided into a slight deviation, an error between 5% and 10% can be divided into a medium deviation, and an error greater than 10% can be divided into a severe deviation. Then, according to the preset curing machine parameter adjustment rules, such as the light intensity adjustment rules, the light time adjustment rules, etc., the adjustment parameters are matched. For example, if the curing degree error is a medium deviation, the light intensity can be increased by 10%. The light source parameters of the intelligent dental light curing machine are adjusted. For example, the control panel of the light curing machine can be manually adjusted, or control instructions can be sent through computer software to adjust the light intensity, light time and other parameters of the light source. For example, if the light source parameter adjustment value is to increase the light intensity by 10%, the staff can increase the light intensity by 10% through the control panel or computer software. The state information after the light source parameters are adjusted is stored to generate light source control state data. For example, it can be determined whether the light curing has reached the termination state based on the preset light curing time or the resin curing degree threshold. For example, if the preset light curing time is 60 seconds, when the light curing time reaches 60 seconds, the light curing is determined to be terminated. When the light curing is terminated, the light beam modulation unit installed in step S41 is used to terminate the photoacoustic signal collection. For example, a short laser pulse can be emitted by a light beam modulation unit, and the photoacoustic signal can be collected by a photoacoustic detector. Then, the collected photoacoustic signal is analyzed, for example, characteristic parameters such as the photoacoustic signal amplitude and the photoacoustic signal frequency are calculated, and the resin curing quality is evaluated according to a pre-established photocuring quality evaluation model, such as a model based on a support vector machine or a neural network, to generate photocuring quality evaluation data. For example, the characteristic parameters of the photoacoustic signal can be input into a trained support vector machine model to output the corresponding resin curing quality score.

[0191] The present invention also provides a real-time monitoring system for an intelligent medical dental light curing machine, which executes the real-time monitoring method for an intelligent medical dental light curing machine as described above. The real-time monitoring system for an intelligent medical dental light curing machine comprises:

[0192] The multi-view light field acquisition module is used to use the intelligent dental light curing machine to irradiate the target dental light curing area with a target area light source to generate multi-view light intensity signal data; perform light field matrix processing on the multi-view light intensity signal data to generate multi-view light field matrix data;

[0193] The curing light intensity analysis module is used to obtain the parameters of the resin curing material; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; use the resin curing material parameters to perform light propagation path analysis on the light field voxel space data to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data;

[0194] The resin curing prediction module is used to process the initial surface light intensity distribution data of the resin surface through the light curing ray tracing data to generate the surface light intensity energy distribution data; and to predict the resin photosensitization curing of the surface light intensity energy distribution data using the resin curing material parameters to generate the resin curing degree prediction curve data;

[0195] The curing degree real-time monitoring module is used to perform real-time photoacoustic detection of the target dental light-curing area using the intelligent dental light-curing machine to generate multi-wavelength laser feedback signal data; perform resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extract the curing degree of key areas; and generate actual resin curing degree data;

[0196] The curing quality assessment module is used to calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data, and generate the curing degree error curve data; the light source parameters of the intelligent dental light curing machine are adjusted through the curing degree error curve data, and the light curing quality assessment is terminated to generate the light curing quality assessment data.

[0197] The beneficial effect of the present application is that, by deploying a microlens array on the light curing machine irradiation head, and combining multi-angle light intensity signal acquisition and light field matrix construction, the light in the target area is accurately captured and reconstructed in three dimensions, and complete light field data containing light intensity, angle and position information is constructed. On this basis, combined with the optical parameters of the resin material, the Monte Carlo ray tracing algorithm is used to simulate the propagation path of light inside the resin, and the light intensity energy distribution at each position on the surface and inside of the resin is accurately calculated. The beam modulation unit is integrated into the light curing machine, and the multi-wavelength laser pulse is emitted to the target area using a preset laser excitation sequence. By real-time acquisition of photoacoustic signals, photoacoustic image reconstruction, spectrum analysis and feature extraction, multi-parameter information such as the photoacoustic absorption coefficient, photoacoustic scattering coefficient and photoacoustic velocity of the resin is obtained, and a real-time dynamic tomography image is constructed. Then, a relationship model between the degree of resin curing and the multi-parameter photoacoustic characteristics is established using a machine learning algorithm to achieve real-time quantitative evaluation of the actual degree of resin curing. The resin curing degree prediction curve is aligned with the actual curing degree curve for time axis and error calculation, and a graded warning is performed according to the degree of error. Then, according to the preset curing machine parameter adjustment rules, such as light intensity adjustment rules, light time adjustment rules, etc., the adjustment parameters are automatically matched, and the staff is guided to adjust the light source parameters of the light curing machine. After the light curing is completed, the system will also use the beam modulation unit to terminate the photoacoustic signal acquisition, and evaluate the resin curing quality according to the pre-established light curing quality evaluation model to generate light curing quality evaluation data. Thereby achieving closed-loop control of the resin curing process to ensure that the resin achieves the expected curing effect. It solves the problem that traditional light curing methods are difficult to accurately control the curing effect, and improves the accuracy and efficiency of dental restoration treatment.

[0198] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0199] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A real-time monitoring method for an intelligent medical dental light curing machine, characterized in that: The following steps are involved: Step S1: using an intelligent dental light curing machine to irradiate a target dental light curing area with a target area light source to generate multi-viewing angle light intensity signal data; Performing light field matrix processing on the multi-view light intensity signal data to generate multi-view light field matrix data; Step S2: Acquire resin curing material parameters; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; perform light propagation path analysis on the light field voxel space data using the resin curing material parameters to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data; Step S3: processing the initial surface light intensity distribution data for resin surface light intensity distribution by light-curing ray tracing data to generate surface light intensity energy distribution data; predicting resin photosensitization by using resin curing material parameters to generate resin curing degree prediction curve data; Step S4: using the intelligent dental light curing machine to perform real-time photoacoustic detection on the target dental light curing area to generate multi-wavelength laser feedback signal data; performing resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin; Step S5: Calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data to generate curing degree error curve data; adjust the light source parameters of the intelligent dental light curing machine using the curing degree error curve data, and perform termination light curing quality evaluation to generate light curing quality evaluation data.

2. The real-time monitoring method for an intelligent medical dental light curing machine according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: deploying a microlens annular array on the irradiation head of the intelligent dental light curing machine, and installing a photoelectric sensor directly behind each microlens, so as to obtain annular microlens array data; Step S12: performing microlens array angle calibration according to the annular microlens array data to generate microlens array calibration parameters; Step S13: irradiating the target dental light-curing area with the intelligent dental light-curing machine based on the microlens array calibration parameters, and synchronously collecting multi-viewing angle light intensity signals with the photoelectric sensor to generate multi-viewing angle light intensity signal data; Step S14: extracting light field parameters according to the microlens array calibration parameters to obtain light irradiation field parameters; Step S15: Establishing a mapping relationship between the light intensity value of each microlens and the light direction in three-dimensional space by using the light irradiation field parameters and the multi-viewing angle light intensity signal data through the annular microlens array data, thereby constructing the multi-viewing angle light direction matrix data; Step S16: performing light field matrix processing according to the multi-viewing angle light direction matrix data and the multi-viewing angle light intensity signal data to generate multi-viewing angle light field matrix data.

3. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire resin curing material parameters, wherein the resin curing material parameters include resin material optical parameters and resin material light curing parameters; Step S22: performing three-dimensional voxel grid processing on the target dental light-curing area to generate voxel grid data of the curing area; Step S23: performing multi-view light space projection on the solidified area voxel grid data through the multi-view light field matrix data to generate light voxel mapping data; Step S24: performing voxel light feature aggregation according to the light voxel mapping data, and performing light field voxel space processing to obtain light field voxel space data; Step S25: performing light propagation path analysis on the light field voxel space data by using the optical parameters of the resin material to generate light-curing ray tracing data; Step S26: performing resin surface endpoint analysis on the light-curing ray tracing data, and performing a ray endpoint neighborhood search to obtain surface area ray cluster data; Step S27: Calculate the neighborhood average light intensity value of the surface area light cluster data to generate initial surface light intensity distribution data.

4. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 3, characterized in that: Step S25 includes the following steps: Step S251: assigning light field characteristic parameters to light field voxel spatial data based on the optical parameters of the resin material to generate voxel optical parameter matrix data, wherein the voxel optical parameter matrix data includes voxel refractive index coefficient, voxel absorption coefficient and voxel scattering coefficient; Step S252: performing voxel light field propagation update on the light field voxel space data through the voxel optical parameter matrix data to generate stable iterative light field data; Step S253: identifying light source voxel units according to the stable iterative light field data, marking them as root nodes of the light propagation trajectory, and generating light source node data; Step S254: tracing the voxel light direction of the stable iterative light field data through the light source node data to generate light propagation vector data; Step S255: Based on the light propagation vector data, the light is propagated through each voxel unit marked as a branch node of the light trajectory by stable iteration of the light field data, thereby obtaining light path chain data; Step S256: using the preset light propagation termination condition to identify the light propagation termination voxel of the light path chain data, thereby obtaining the photo-stereolithography ray tracing data.

5. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 4, characterized in that: Step S252 includes the following steps: Step S2521: constructing a voxel interface optical interaction model for the voxel optical parameter matrix data using the Fresnel formula to generate a voxel optical interaction model; Step S2522: constructing a voxel internal light attenuation model for the voxel optical parameter matrix data using the Beer-Lambert law to generate a voxel internal attenuation model; Step S2523: discretizing the light propagation path according to the voxel optical interaction model and the voxel internal attenuation model, and establishing an iterative calculation model of light propagation between voxels through finite element analysis to obtain a light propagation iterative equation group; Step S2524: performing light propagation operator calculation according to the light propagation iterative equation group to obtain light propagation operator data; Step S2525: injecting the light field voxel space data as the initial light field, and using the light propagation operator data to perform voxel light propagation simulation to obtain voxel light propagation update data; Step S2526: performing light field iterative updating on the light field voxel space data according to the voxel light propagation update data to generate voxel iterative light field data; Step S2527: performing adjacent updated light field difference analysis on the voxel iterative light field data to generate iterative light field difference data, wherein the iterative light field difference data includes voxel light intensity difference data, voxel light direction difference data and light field energy difference data; Step S2528: Based on the preset stability index threshold, the light field stability of the voxel iterative light field data is judged by iterating the light field difference data. When the difference between two adjacent iterative light fields is less than or equal to the preset stability index threshold, the light field iterative update ends and the voxel iterative light field data is marked as stable iterative light field data; otherwise, return to step S2526 for light field iterative simulation.

6. The real-time monitoring method for an intelligent medical dental light curing machine according to claim 3, characterized in that: Step S3 includes the following steps: Step S31: performing surface voxel energy attenuation analysis on the photocuring ray tracing data to obtain path energy attenuation ratio data; Step S32: performing resin surface energy contribution weight processing according to the path energy attenuation ratio data, and performing voxel point cumulative light intensity energy calculation on the voxel grid data of the solidified area to generate cumulative light intensity energy contribution data; Step S33: performing resin surface light intensity distribution processing on the initial surface light intensity distribution data by accumulating light intensity energy contribution data to generate surface light intensity energy distribution data; Step S34: performing resin curing state parameter processing according to the resin material light curing parameters to generate curing state parameter data; Step S35: obtaining a photosensitizer reaction rate equation, establishing a mapping relationship between the resin curing state parameter and the photosensitizer concentration according to the photosensitizer reaction rate equation and the curing state parameter data, and generating curing state parameter mapping data; Step S36: Calculating the cumulative light dose on the resin surface based on the surface light intensity energy distribution data in a preset time window to generate cumulative light dose prediction sequence data; Step S37: using the curing state parameter mapping data to perform a time series analysis of the photosensitivity concentration on the cumulative light dose prediction sequence data, and performing a quantification process on the resin curing degree to generate resin curing degree prediction curve data.

7. The real-time monitoring method for an intelligent medical dental light curing machine according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: A beam modulation unit is integrated in the intelligent dental light curing machine, and the beam modulation unit is located inside the handheld operating handle near the light guide output head; Step S42: emitting multi-wavelength laser pulses to the target dental light-curing area by using a beam modulation unit through a preset set of laser excitation sequences, and performing real-time photoacoustic detection to generate multi-wavelength laser feedback signal data; Step S43: performing photoacoustic image fusion processing on the multi-wavelength laser feedback signal data to generate real-time dynamic tomography image data; Step S44: performing resin curing feature vector processing on the multi-parameter feature value corresponding to each pixel point in the real-time dynamic tomography image data to generate curing parameter vector space data; Step S45: Calculate the curing degree according to the curing parameter vector space data to generate resin curing degree data; Step S46: performing solidification parameter clustering processing on the real-time dynamic tomography image data through the resin solidification degree data, and performing solidification region boundary recognition to generate solidification region boundary data; Step S47: marking the curing state label according to the curing area boundary data, and extracting the curing degree of the key area; generating the actual curing degree data of the resin.

8. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 1, characterized in that: Step S43 includes the following steps: Step S431: performing photoacoustic signal preprocessing on the multi-wavelength laser feedback signal data, and performing wavelet coefficient denoising processing to obtain photoacoustic signal denoising wavelet coefficients; Step S432: extracting the target component of the photoacoustic signal from the multi-wavelength laser feedback signal data by using the photoacoustic signal denoising wavelet coefficient to generate target photoacoustic component data; Step S433: performing photoacoustic image reconstruction on the target photoacoustic component data based on a delay and sum algorithm to generate two-dimensional resin tomographic image sequence data; Step S434: performing pixel point photoacoustic signal spectrum analysis on the two-dimensional resin tomographic image sequence data to generate photoacoustic spectrum characteristic diagram data; Step S435: extracting multi-dimensional photoacoustic features of the resin according to the photoacoustic spectrum feature map data, and obtaining a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively; Step S436: performing pixel point photoacoustic velocity estimation on the photoacoustic spectrum characteristic map data based on the two-dimensional resin tomographic image sequence data to generate a photoacoustic velocity map; Step S437: using the photoacoustic velocity map, the photoacoustic absorption coefficient map and the photoacoustic scattering coefficient map to perform multi-parameter feature value combination on the two-dimensional resin tomographic image sequence data, and perform resin curing feature weight fusion to generate real-time dynamic tomographic image data.

9. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: aligning the time axis of the actual curing degree data of the resin based on the resin curing degree prediction curve data, and performing actual curing degree curve processing to generate actual curing degree curve data; Step S52: using the resin curing degree prediction curve data to calculate the curing degree error of the actual curing degree curve data, and generating curing degree error curve data; Step S53: performing graded warning of the degree of deviation on the curing degree error curve data, and adjusting the parameters through preset curing machine parameter adjustment rules to generate light source parameter adjustment values; Step S54: the staff controls the light source parameters of the intelligent dental light curing machine through the light source parameter adjustment value to generate light source control state data; Step S55: determining the photocuring termination state according to the light source control state data; when the photocuring is terminated, collecting the termination photoacoustic signal by using the light beam modulation unit, and performing a curing quality assessment to generate photocuring quality assessment data.

10. A real-time monitoring system for an intelligent medical dental light curing machine, characterized in that: Used to execute the real-time monitoring method of the intelligent medical dental light curing machine according to claim 1, the real-time monitoring system of the intelligent medical dental light curing machine comprises: The multi-view light field acquisition module is used to use the intelligent dental light curing machine to irradiate the target dental light curing area with a target area light source to generate multi-view light intensity signal data; perform light field matrix processing on the multi-view light intensity signal data to generate multi-view light field matrix data; The curing light intensity analysis module is used to obtain the parameters of the resin curing material; perform light field voxel space processing on the target dental light curing area through multi-view light field matrix data to obtain light field voxel space data; use the resin curing material parameters to perform light propagation path analysis on the light field voxel space data to generate light curing ray tracing data; calculate the resin surface light intensity value based on the light curing ray tracing data to generate initial surface light intensity distribution data; The resin curing prediction module is used to process the initial surface light intensity distribution data of the resin surface through the light curing ray tracing data to generate the surface light intensity energy distribution data; and to predict the resin photosensitization curing of the surface light intensity energy distribution data using the resin curing material parameters to generate the resin curing degree prediction curve data; The curing degree real-time monitoring module is used to perform real-time photoacoustic detection of the target dental light-curing area using the intelligent dental light-curing machine to generate multi-wavelength laser feedback signal data; perform resin curing feature vector processing on the multi-wavelength laser feedback signal data, and extract the curing degree of key areas; and generate actual resin curing degree data; The curing quality assessment module is used to calculate the curing degree error of the actual curing degree data of the resin using the resin curing degree prediction curve data, and generate the curing degree error curve data; the light source parameters of the intelligent dental light curing machine are adjusted through the curing degree error curve data, and the light curing quality assessment is terminated to generate the light curing quality assessment data.

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