A Real-time Monitoring Method and System for a Dental Light Curing Machine for Intelligent Medical Treatment

Abstract

The present invention relates to the technical field of photocuring monitoring, and particularly to a real-time monitoring method and system for an intelligent medical dental photocuring machine. The method includes the following steps: calculating the light intensity value on the resin surface of the target dental photocuring area to generate initial surface light intensity distribution data; predicting the resin photosensitive curing of 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 the intelligent dental photocuring machine to generate resin actual curing degree data; calculating the curing degree error of the resin actual curing degree data by using the resin curing degree prediction curve data, and performing light source parameter regulation to generate photocuring quality evaluation data. The present invention realizes the real-time monitoring of the dental photocuring process and the intelligent regulation of the light source parameters by dynamically tracking the photocuring error, effectively solves the problems of insufficient curing or over-curing in the photocuring process, and significantly improves the photocuring quality and treatment efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of photocuring monitoring, and particularly to a real-time monitoring method and system for a dental photocuring machine for intelligent medical use. Background Art

[0002] As an efficient and convenient clinical restoration method, dental photocuring technology has been widely used in multiple fields such as dental caries restoration, teeth whitening, and orthodontic treatment. Its principle is to use light of a specific wavelength to irradiate a photosensitive resin material, causing a photoinitiator to generate free radicals, which in turn initiate the polymerization reaction of resin monomers, ultimately achieving the transformation of the material from a liquid state to a solid state. The core of photocuring technology lies in the performance of the photocuring machine, and parameters such as the light intensity, wavelength, and irradiation time emitted by it directly affect the curing effect of the resin material, and thus affect the mechanical properties, biocompatibility, and service life of the restoration. An ideal curing effect requires the resin material to be cured fully and evenly, avoiding the situation of under-curing or over-curing. Due to the complex oral environment, it is difficult to accurately control the photocuring depth, light path, and curing uniformity, and problems of under-curing or over-curing are likely to occur. However, most traditional dental photocuring machines adopt an open-loop control method, only irradiating according to preset light source parameters and relying on experience to judge the curing time, unable to accurately evaluate the actual curing degree of the resin and 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 a dental photocuring machine for intelligent medical use to solve at least one of the above technical problems.

[0004] To achieve the above object, a real-time monitoring method for a dental photocuring machine for intelligent medical use includes the following steps:

[0005] Step S1: Use an intelligent dental photocuring machine to perform target area light source irradiation on a target dental photocuring area 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;

[0006] Step S2: Obtain resin curing material parameters; perform light field voxel space processing on the target dental photocuring area through the 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 photocuring ray tracing data; calculate the resin surface light intensity value according to the photocuring ray tracing data to generate initial surface light intensity distribution data;

[0007] Step S3: Process the initial surface light intensity distribution data through the resin surface light intensity distribution using the light-curing ray tracing data to generate the surface light intensity energy distribution data; predict the resin photosensitive curing using the resin curing material parameters for the surface light intensity energy distribution data to generate the resin curing degree prediction curve data;

[0008] Step S4: Use an 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; process the multi-wavelength laser feedback signal data for resin curing eigenvector processing and extract the curing degree of the key area; generate the resin actual curing degree data;

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

[0010] The present invention irradiates a target dental light-curing area with a smart dental light-curing machine, which can ensure that the light uniformly covers the area to be processed, provide information on the light intensity at different angles, and help comprehensively understand the propagation characteristics of light in resin materials. Specific parameters of the resin curing material are obtained, including important properties such as light sensitivity and refractive index. These parameters are crucial for subsequent analysis of the light propagation path. By performing light field voxel space processing on the multi-view light field matrix data, the complex light field distribution can be converted into voxel space data that is easy to calculate. Using the resin curing material parameters to analyze the light propagation path of this data can describe in detail the propagation process of light in the resin. Based on this data, the light intensity value on the resin surface is calculated, and the connection between the lighting 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 map. This helps better understand which areas receive sufficient energy to complete the curing process and which areas are insufficient. Combining with the resin material properties, using this detailed surface light intensity energy distribution data to predict the resin photosensitive curing can intuitively display the expected curing level during the whole process, which is beneficial to guiding the adjustment of the lighting strategy in actual operation to ensure the best curing effect. The real-time photoacoustic detection technology is used to collect multi-wavelength laser feedback signal data, aiming to monitor the changes of the resin material under the action of light in real time, extract the key vector information about the resin curing characteristics, and then determine the actual curing degree of the material in the current state. Compared with the traditional method, this method can obtain accurate results faster and understand the progress without waiting for the end of the whole curing process. By comparing the predicted and actual curing degrees, the deviation during the curing process is identified, and the light source parameters of the smart dental light-curing machine are adjusted, which can achieve precise control of the light-curing machine, ensure reasonable light energy distribution during the curing process, and improve the curing effect. In addition, the light-curing quality is evaluated to generate light-curing quality evaluation data, which enhances the accuracy and effectiveness of dental light-curing. Therefore, a real-time monitoring method for a smart medical dental light-curing machine according to the present invention irradiates a target dental light-curing area with a light source, captures the light intensity information at different perspectives, uses the resin curing material parameters to analyze the light propagation path of this data, effectively predicts the curing progress of the resin under the given lighting conditions, identifies the deviation between the theoretical prediction and the actual situation through real-time photoacoustic detection, realizes the dynamic tracking and control of the resin curing process, ensures that each part can reach the ideal curing effect, and effectively monitors and guarantees the quality of the whole curing process.

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

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

[0013] Step S12: Calibrate the angles of the microlens array according to the annular microlens array data to generate microlens array calibration parameters;

[0014] Step S13: Based on the microlens array calibration parameters, use the intelligent dental light-curing machine to irradiate the target dental light-curing area, and use the photoelectric sensor to synchronously collect multi-view light intensity signals to generate multi-view light intensity signal data;

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

[0016] Step S15: Use the annular microlens array data, the light irradiation field domain parameters, and the multi-view light intensity signal data to establish a mapping relationship between the light intensity value of each microlens and the three-dimensional space light direction, thereby constructing multi-view light direction matrix data;

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

[0018] In the present invention, a microlens annular array is deployed in the irradiation head of the intelligent dental light-curing machine, and a photoelectric sensor is installed directly behind each microlens, which can realize multi-angle light illumination and signal collection of the target area, improving the uniformity and coverage of the light illumination. Calibrating the angles of the microlens array according to the annular microlens array data ensures that the angles and positions of each microlens are in the best state, thus guaranteeing the consistency and accuracy of the light illumination, making the light illumination more precisely focused on the target area and reducing the light illumination deviation. By irradiating through the calibrated microlens array and combining with the real-time monitoring of the photoelectric sensor, the light intensity distribution at multiple angles can be obtained. By extracting specific light field domain parameters through the calibration parameters, the distribution of the light illumination in the target area can be described in detail. Associating the light intensity value with the light direction forms a detailed three-dimensional light field model. By constructing multi-view light direction matrix data, the light illumination effect can be more precisely controlled and predicted. By integrating the multi-view light direction matrix data and the multi-view light intensity signal data to generate a comprehensive light field matrix, this matrix contains detailed information on the light intensity and direction. Such multi-view light field matrix data can comprehensively reflect the light illumination situation of the intelligent dental curing machine.

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

[0020] Step S21: Obtain resin curing material parameters, where the resin curing material parameters include resin material optical parameters and resin material photocuring parameters;

[0021] Step S22: Perform three-dimensional voxel grid processing on the target dental photocuring area to generate cured area voxel grid data;

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

[0023] Step S24: Aggregate voxel light ray features according to the light ray voxel mapping data and perform light field voxel space processing to obtain light field voxel space data;

[0024] Step S25: Analyze the light ray propagation path of the light field voxel space data through the resin material optical parameters to generate photocuring light ray tracing data;

[0025] Step S26: Analyze the resin surface end points of the photocuring light ray tracing data and perform light ray end point neighborhood search to obtain surface neighborhood light ray cluster data;

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

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

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

[0029] Step S251: Assign light field characteristic parameters to the light field voxel space data based on the optical parameters of the resin material to generate voxel optical parameter matrix data, where the voxel optical parameter matrix data includes voxel refractive index coefficients, voxel absorption coefficients, and voxel scattering coefficients;

[0030] Step S252: Perform 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: Identify the light source voxel unit according to the stable iterative light field data and mark it as the root node of the light ray propagation trajectory to generate light source node data;

[0032] Step S254: Perform voxel light ray direction tracking on the stable iterative light field data through the light source node data to generate light ray propagation vector data;

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

[0034] Step S256: Use a preset light ray propagation termination condition to identify the light ray propagation termination voxel for the light ray path chain data, thereby obtaining light curing light ray tracing data.

[0035] In the present invention, by assigning the optical characteristics of the resin material to each voxel unit, a detailed optical parameter matrix is constructed. 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 rays in the material. This iterative update method can gradually converge to a stable light field state, ensuring that the simulation results of light ray 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 points or root nodes of light ray propagation. This marking method clarifies the starting point of light ray propagation. Based on the marked light source nodes, the propagation direction of light rays in each voxel unit can be traced, and the path of light rays in the material can be detailedly recorded to form light ray propagation vector data. By tracking the light ray propagation vector, the path nodes passed by the light ray can be marked in each voxel unit to form a complete light ray path chain, which detailedly records each intermediate node of the light ray from the light source to the end point. By setting reasonable light ray propagation termination conditions (such as the light intensity being lower than a certain threshold or reaching the material boundary), the end point of light ray propagation can be determined, and the termination position of light ray propagation can be accurately identified to generate the final light curing light ray tracing data. These tracing data not only describe the complete path of light rays 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: Construct 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: Construct a voxel internal light attenuation model for the voxel optical parameter matrix data using the Lambert-Beer law to generate a voxel internal attenuation model;

[0039] Step S2523: Perform discretization processing on the light propagation path according to the voxel optical interaction model and the voxel internal attenuation model, and establish an iterative calculation model for light propagation between voxels through finite element analysis to obtain an iterative light propagation equation set;

[0040] Step S2524: Calculate the light propagation operator according to the iterative light propagation equation set to obtain light propagation operator data;

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

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

[0043] Step S2527: Analyze the difference between adjacent updated light fields of the voxel iterative light field data to generate iterative light field difference data, where 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: Determine the light field stability of the voxel iterative light field data based on the preset stability index threshold through the iterative light field difference data. When the difference between adjacent two 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] By applying Fresnel's formula, the present invention can describe in detail the reflection and refraction behavior of light at the interface of different media. This model can accurately simulate the interaction between the inside and outside interfaces of resin materials. By applying Lambert-Beer's law, the attenuation of light during propagation inside the resin material can be calculated. This model can describe the absorption and scattering processes of light inside the material. By discretizing the light propagation path and combining the optical interaction model and the internal attenuation model, a complete iterative calculation model for light propagation can be established. This model can describe in detail the propagation process of light in each voxel unit, forming an iterative equation system for light propagation. By solving the iterative equation system for light propagation, light propagation operator data can be obtained, which describes the propagation characteristics of light in each voxel unit, including phenomena such as reflection, refraction, absorption, and scattering. By injecting the initial light field data into the system and using the light propagation operator for simulation, the light field information in each voxel unit can be updated, and the propagation process of light in the material can be gradually simulated. By applying the voxel light propagation update data to the light field voxel space data, iterative update of the light field can be performed, gradually approaching the actual light field distribution and generating voxel iterative light field data. By performing differential analysis on the light field data of two adjacent iterations, the changes after each iteration can be evaluated. By setting a stable index threshold and making a stability determination 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, iterative simulation needs to continue until a stable state is reached, ensuring that the final light field data has high precision and reliability.

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

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

[0048] Step S32: Perform resin surface energy contribution weight processing based on the path energy attenuation ratio data, and perform voxel point cumulative light intensity energy calculation on the cured area voxel grid data to generate cumulative light intensity energy contribution data;

[0049] Step S33: Perform resin surface light intensity distribution processing on the initial surface light intensity distribution data through the cumulative light intensity energy contribution data to generate surface light intensity energy distribution data;

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

[0051] Step S35: Obtain the photosensitizer reaction rate equation, establish the mapping relationship between the resin curing state parameters and the photosensitizer concentration according to the photosensitizer reaction rate equation and the curing state parameter data, and generate the curing state parameter mapping data;

[0052] Step S36: Calculate the cumulative light dose on the resin surface through the surface light intensity energy distribution data based on a preset time window, and generate the cumulative light dose prediction sequence data;

[0053] Step S37: Perform photosensitive concentration time series analysis on the cumulative light dose prediction sequence data by using the curing state parameter mapping data, and perform resin curing degree quantification processing to generate resin curing degree prediction curve data.

[0054] By analyzing the energy attenuation of light during propagation, the present invention can determine the energy loss ratio of each ray when it reaches the resin surface. By considering the energy attenuation ratio of each ray, the corresponding energy contribution weight can be assigned to each voxel. Then, by cumulatively calculating the energy of all voxels, the total light intensity energy contribution of each voxel 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 map, which can comprehensively reflect the actual light illumination of each point on the resin surface. By analyzing the photocuring parameters of the resin material, such as curing depth, curing time, etc., the parameters related to the curing state can be extracted. These parameters describe the curing behavior of the resin material under different light illumination conditions. By introducing the photosensitizer reaction rate equation, the resin curing state parameters can be related to the photosensitizer concentration. By setting a time window, the cumulative light dose received by the resin surface within this time period can be calculated, which can dynamically track the light illumination of the resin surface and 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 the photosensitizer concentration over time can be analyzed, and the curing degree of the resin can be quantified accordingly.

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

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

[0057] Step S42: Use the beam modulation unit to emit multi-wavelength laser pulses to the target dental photocuring area through a preset set of laser excitation sequences, and perform real-time photoacoustic detection to generate multi-wavelength laser feedback signal data;

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

[0059] Step S44: Process the multi-parameter eigenvalues corresponding to each pixel point in the real-time dynamic tomography image data through a resin curing feature vector to generate cured parameter vector space data;

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

[0061] Step S46: Perform cured parameter clustering processing on the real-time dynamic tomography image data through the resin curing degree data, and perform cured area boundary recognition to generate cured area boundary data;

[0062] Step S47: Mark the cured state labels according to the cured area boundary data, and extract the cured degree of the key area; generate the actual resin cured degree data.

[0063] Through the use of a preset laser excitation sequence, the present invention can perform laser irradiation on the resin material at multiple wavelengths, thereby obtaining feedback signals at different wavelengths. This multi-wavelength laser pulse emission method can comprehensively capture the reaction of the resin material under different lighting conditions. By fusing the multi-wavelength laser feedback signals, a high-resolution photoacoustic image can be constructed. By processing the multi-parameter eigenvalues of each pixel point, the feature vectors related to resin curing can be extracted, which describe the curing state of the resin at different positions and depths, forming a detailed cured parameter vector space. By analyzing the cured parameter vector space data, the curing degree of the resin at different positions can be quantified, and specific curing percentages or curing indices can be provided, reflecting the actual curing state of the resin under the current lighting conditions. Using the resin curing degree data, pixel points in the real-time dynamic tomography image can be clustered to distinguish different cured areas. By labeling the cured area boundary data, corresponding cured state labels (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 cured degree data of these areas are extracted.

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

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

[0066] Step S432: Extract the target photoacoustic component of the multi-wavelength laser feedback signal data through the denoised photoacoustic signal wavelet coefficients to generate target photoacoustic component data;

[0067] Step S433: Reconstruct the photoacoustic image based on the delay and sum algorithm for the target photoacoustic component data to generate two-dimensional resin tomography image sequence data;

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

[0069] Step S435: Perform multi-dimensional resin photoacoustic feature extraction based on the photoacoustic spectrum feature map data to obtain a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively;

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

[0071] Step S437: Use the photoacoustic velocity map, the photoacoustic absorption coefficient map, and the photoacoustic scattering coefficient map to perform multi-parameter eigenvalue 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] Through preprocessing and wavelet coefficient denoising processing, the present invention can remove the noise and interference in the signal, improve the signal-to-noise ratio of the signal, be able to retain the effective components in the signal, and at the same time reduce the influence of background noise. Using the denoised photoacoustic signal wavelet coefficients, the effective signal components related to resin curing can be extracted, the target signal can be separated, the irrelevant noise can be excluded, and it is ensured that the extracted components are the true reactions 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 spatial domain. By performing spectrum analysis on the photoacoustic signal of each pixel point, the information in the frequency domain can be extracted, which can reveal the distribution of different frequency components in the image and provide more physical information. By analyzing the photoacoustic spectrum feature map data, the photoacoustic absorption coefficient and the photoacoustic scattering coefficient can be extracted, which describe the absorption and scattering characteristics of the resin material to light and provide important optical parameters. By jointly analyzing the two-dimensional resin tomographic image sequence data and the photoacoustic spectrum feature map data, the photoacoustic velocity of each pixel point can be estimated, which can provide the distribution of the sound velocity inside the resin material and reflect the physical properties of the material. By combining the photoacoustic velocity map, the photoacoustic absorption coefficient map, and the photoacoustic scattering coefficient map, multi-parameter eigenvalue combination can be performed on the two-dimensional resin tomographic image sequence data. Then, through the weight fusion method, considering the importance of each parameter comprehensively, comprehensive information on the internal structure and curing state of the resin material is provided.

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

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

[0075] Step S52: Calculate the curing degree error of the actual curing degree curve data by using the resin curing degree prediction curve data to generate curing degree error curve data;

[0076] Step S53: Classify and give early warnings for the deviation degree of the curing degree error curve data, and match the adjustment parameters through the preset curing machine parameter adjustment rules to generate the light source parameter adjustment value;

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

[0078] Step S55: Determine the light curing termination state according to the light source adjustment state data. When the light curing terminates, use the beam modulation unit to collect the termination photoacoustic signal and conduct a curing quality assessment to generate light curing quality assessment data.

[0079] By aligning the predicted curing degree curve with the actually measured curing degree data on the time axis, the present invention can ensure that the two are compared under the same time reference, eliminate the errors caused by time asynchronization, 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, which can detail the difference between the predicted value and the actual value during the curing process and provide specific error information. By analyzing the curing degree error curve data, the errors can be divided into different levels and classified and warned according to the preset warning rules. At the same time, based on these error information and combined with the preset curing machine parameter adjustment rules, the corresponding light source parameter adjustment value can be generated. According to the generated light source parameter adjustment value, the staff can manually or automatically adjust the light source parameters of the intelligent dental light curing machine, such as light intensity, wavelength, etc., which can adjust the light conditions in real time to meet the current curing requirements, ensure that the light conditions are always in the best state, thereby improving the uniformity and consistency of resin curing. By analyzing the light source adjustment state data, it can be judged whether the light curing process has been completed. Once it is determined that the curing terminates, use the beam modulation unit to collect the photoacoustic signal at the termination moment and conduct 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 an intelligent medical dental light curing machine real-time monitoring system, which executes the intelligent medical dental light curing machine real-time monitoring method as described above. The intelligent medical dental light curing machine real-time monitoring system includes:

[0081] The multi - perspective light field acquisition module is used to irradiate the target dental light - curing area with a light source of the target area by using an intelligent dental light - curing machine, generating multi - perspective light intensity signal data; performing light field matrix processing on the multi - perspective light intensity signal data to generate multi - perspective light field matrix data;

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

[0083] The resin curing prediction module is used to perform resin surface light intensity distribution processing on the initial surface light intensity distribution data through the light - curing ray tracing data to generate surface light intensity energy distribution data; predict the resin photosensitive curing by using the resin curing material parameters for the surface light intensity energy distribution data to generate resin curing degree prediction curve data;

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

[0085] The curing quality evaluation module is used to calculate the curing degree error of the resin actual curing degree data by using the resin curing degree prediction curve data to generate curing degree error curve data; regulate the light source parameters of the intelligent dental light - curing machine through the curing degree error curve data and perform termination light - curing quality evaluation to generate light - curing quality evaluation data. Description of the Drawings

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

[0087] Figure 2 is Figure 1 a detailed implementation step - flow diagram of step S3 in;

[0088] Figure 3 is Figure 1 a detailed implementation step - flow diagram of step S4 in;

[0089] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0090] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts belong to 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 drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the 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 in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0092] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. 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 related items.

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

[0094] Step S1: Use an intelligent dental light-curing machine to irradiate a target dental light-curing area with a light source in the target area 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;

[0095] Step S2: Obtain resin curing material parameters; perform light field voxel space processing on the target dental light-curing area through the multi-view light field matrix data to obtain light field voxel space data; use the resin curing material parameters to perform an analysis of the light propagation path of 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;

[0096] Step S3: Perform resin surface light intensity distribution processing on the initial surface light intensity distribution data through the light-curing ray tracing data to generate surface light intensity energy distribution data; use the resin curing material parameters to perform resin photosensitive curing prediction on the surface light intensity energy distribution data to generate resin curing degree prediction curve data;

[0097] Step S4: Use an 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; process the multi-wavelength laser feedback signal data for resin curing eigenvector and extract the curing degree of the key area; generate resin actual curing degree data;

[0098] Step S5: Use the resin curing degree prediction curve data to calculate the curing degree error of the resin actual curing degree data to generate curing degree error curve data; regulate the light source parameters of the intelligent dental light-curing machine through the curing degree error curve data and conduct termination light-curing quality assessment to generate light-curing quality assessment data.

[0099] In the embodiment of the present invention, with reference to Figure 1 As described, it is a schematic diagram of the step flow 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: Use an intelligent dental light-curing machine to irradiate the target area light source on the target dental light-curing area to generate multi-viewpoint light intensity signal data; process the multi-viewpoint light intensity signal data for the light field matrix to generate multi-viewpoint light field matrix data;

[0101] In the embodiment of the present invention, the intelligent dental light-curing machine is equipped with multiple LED light sources, and each light source can independently control the light intensity and irradiation angle. Taking the filling of a target tooth cavity as an example, first place the filling resin material in the cavity. Start the intelligent dental light-curing machine and use the preset multi-viewpoint lighting program to sequentially control multiple LED light sources to irradiate the target cavity area. When each light source irradiates, use the built-in light intensity sensor to collect the light intensity signal and record the light source position and angle information. For example, 5 LED light sources can be set to irradiate the cavity from five angles of 0°, 45°, 90°, 135°, and 180°, with each irradiation time of 1 second and the sensor sampling frequency of 100 Hz. Input the collected multi-viewpoint light intensity signal data into the computer and use the light field imaging principle to construct a 4D light field matrix. Specifically, a light field rendering software, such as Light FieldToolbox, can be used to map the light intensity signal data of each viewpoint 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: Obtain the 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; use the resin curing material parameters to analyze the light propagation path of the light field voxel space data to generate light-curing ray tracing data; calculate the resin surface light intensity value according to the light-curing ray tracing data to generate initial surface light intensity distribution data;

[0103] In the embodiment of the present invention, query the product manual of the used resin material to obtain its curing material parameters, such as refractive index, absorption coefficient, scattering coefficient, etc. Import the multi-view light field matrix data generated in step S1 into a computer, and use a light field voxelization algorithm, such as a ray casting algorithm, to discretize the light field matrix into a series of voxels. Each voxel contains information such as light intensity and direction to generate light field voxel space data. For example, the target caries cavity 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, use the Monte Carlo ray tracing algorithm to simulate the light propagation path in the resin material. Specifically, according to the resin material parameters and the light field voxel space data, simulate the scattering, absorption, and refraction processes of photons in each voxel, and trace the propagation path of each ray until the ray leaves the resin material or is completely absorbed. Record the light propagation path information to generate light-curing ray tracing data. Finally, according to the light-curing ray tracing data, calculate the incident light intensity value of each point on the resin surface 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: Perform resin surface light intensity distribution processing on the initial surface light intensity distribution data through the light-curing ray tracing data to generate surface light intensity energy distribution data; use the resin curing material parameters to predict the resin photosensitive curing of the surface light intensity energy distribution data to generate resin curing degree prediction curve data;

[0105] In the embodiments 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 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, an 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 curing degree of the resin at different depths is predicted using the surface light intensity energy distribution data, 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 to calculate the curing degree at each depth and draw a curve of the curing degree changing with the depth.

[0106] Step S4: Use 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; perform resin curing eigenvector processing on the multi-wavelength laser feedback signal data and extract the curing degree of the key area; generate the actual resin curing degree data;

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

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

[0109] In the 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 can be calculated. According to the curing degree error curve data, it is judged whether there is under-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 an under-curing or over-curing problem at that depth. According to the error analysis result, the light source parameters of the intelligent dental photocuring machine are adjusted in real time. For example, if it is found that there is under-curing at the bottom of the dental cavity, 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 dental cavity, 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 resin actual curing degree data. For example, indexes such as average curing degree and curing uniformity are calculated to generate photocuring quality evaluation data. For example, the final resin curing degree distribution map can be compared with the ideal curing degree distribution map to evaluate the photocuring effect and generate a photocuring quality report.

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

[0111] Step S11: Deploy a microlens annular array on the irradiation head of the intelligent dental photocuring machine, and install a photoelectric sensor directly behind each microlens to obtain annular microlens array data;

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

[0113] Step S13: Based on the microlens array calibration parameters, use the intelligent dental photocuring machine to irradiate the target dental photocuring area, and use the photoelectric sensor to synchronously collect multi-view light intensity signals to generate multi-view light intensity signal data;

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

[0115] Step S15: Use the annular microlens array data, the light irradiation field domain parameters, and the multi-view light intensity signal data to establish a mapping relationship between the light intensity value of each microlens and the three-dimensional space light direction, so as to construct multi-view light direction matrix data;

[0116] Step S16: Perform light field matrix processing according to the multi-view light direction matrix data and the multi-view light intensity signal data to generate multi-view 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 with a suitable size is selected, for example, an annular array composed 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 the light passing through the microlens. The irradiation head is reinstalled onto the intelligent dental light-curing machine. By fitting the central position of the image formed by each microlens, the direction vector of the optical axis of the microlens in the coordinate system of the irradiation head 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 direction vector of the optical axis of each microlens is transformed into the external reference coordinate system and compared with the direction vector of the central axis of the irradiation head to calculate the included angle between the optical axis of each microlens and the central axis of the irradiation head, 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 for each layer is 10 seconds. While the light source is irradiating, the photoelectric sensors installed in step S11 are used to synchronously collect the light intensity signals received by each microlens. For example, a data acquisition card can be used to record the output voltage values of each photoelectric sensor at a sampling frequency of 1 kHz. According to the microlens array calibration parameters generated in step S12, the light irradiation range corresponding to each microlens is calculated. For example, according to the focal length and viewing angle of the microlens, the projection range of the microlens on the target area, that is, the light irradiation field corresponding to the microlens, can be calculated. For example, a ray tracing algorithm can be used to simulate the propagation path of the light after passing through the microlens, and calculate the direction vector of the light received by each microlens. 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 corresponding 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 is accumulated into the corresponding voxel, finally obtaining a light field matrix data containing light intensity, angle, and position information.

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

[0119] Step S21: Obtain the resin curing material parameters, where the resin curing material parameters include resin material optical parameters and resin material light-curing parameters;

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

[0121] Step S23: Perform multi-view ray space projection on the voxel grid data of the curing area using the multi-view light field matrix data to generate ray voxel mapping data;

[0122] Step S24: Aggregate voxel ray features based on the ray voxel mapping data and perform light field voxel space processing to obtain light field voxel space data;

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

[0124] Step S26: Analyze the resin surface end point of the light curing ray tracing data and perform ray end point neighborhood search to obtain surface neighborhood ray cluster data;

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

[0126] In the embodiments of the present invention, the product specifications or relevant literature of the resin material used for query are retrieved to obtain its optical parameters and photocuring 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 photocuring parameters of the resin material include the relationship curve between curing depth and light intensity energy, the relationship curve between curing time and light intensity, etc., which describe the curing characteristics of the resin material under light irradiation. Three-dimensional model data of the target dental photocuring area is obtained by using equipment such as a three-dimensional scanner or computed tomography (CT). 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 using three-dimensional modeling software, such as MeshLab, the target area is voxelized, 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 cured 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, the ray tracing algorithm can be used to simulate the light rays emitted from the center of each voxel, passing through the light field matrix until reaching the light source, and recording the penetration point coordinates 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 ray 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. It is also possible to calculate the direction vectors of multiple light rays passing through the same voxel and perform statistical analysis, such as calculating the average value and standard deviation of the light ray directions, to obtain the light ray 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 processes of photons in each voxel are simulated, and the propagation path of each light ray is traced until the light ray leaves the resin material or is completely absorbed. The light ray propagation path information is recorded to generate photocuring ray tracing data. All light ray paths can be traversed to determine whether the end point of the light ray is located within the resin surface voxel. For each light ray end point, a neighborhood search is performed around it, such as searching a spherical area with a radius of 0.5 mm, to find all light rays whose end points are located within this area, generating surface neighborhood light ray cluster data. The intensity values of all light rays in each light ray cluster are averaged to obtain the average light intensity value of the neighborhood of the light ray end point. The coordinates of each light ray end point and its corresponding average light intensity value are associated to generate initial surface light intensity distribution data.

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

[0128] Step S251: Based on the optical parameters of the resin material, assign optical field characteristic parameters to the light field voxel space data to generate voxel optical parameter matrix data, where the voxel optical parameter matrix data includes voxel refractive index coefficient, voxel absorption coefficient, and voxel scattering coefficient;

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

[0130] Step S253: Identify the light source voxel unit according to the stable iterative light field data and mark it as the root node of the light ray propagation trajectory to generate light source node data;

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

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

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

[0134] In the embodiment of the present invention, according to the material properties of each voxel, such as resin type, curing state, etc., corresponding optical parameters are assigned to it. 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 , and the scattering coefficient is set to 1 mm -1 ; the refractive index of the cured resin voxel is set to 1.52, and the absorption coefficient is set to 0.05 mm -1 , and the scattering coefficient is set to 0.8 mm -1, store the optical parameters of each voxel into the corresponding voxel to generate voxel optical parameter matrix data. Use numerical methods such as the finite difference method or the finite element method to simulate the propagation process of light in the voxel space. Specifically, according to the optical parameters of each voxel and the light field information of the surrounding voxels, calculate the change in the light field of this voxel and update the light field value of this voxel. For example, the finite difference format of the Helmholtz equation can be used to calculate the change in light intensity and phase 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, determine the projection area of the light source in the voxel space, and mark all the voxels in this area as light source voxel units. Mark the light source voxel units as the root nodes of the light propagation trajectory to generate light source node data. Use the Monte Carlo method or the ray casting algorithm, etc., to simulate the process of light rays emitted from the light source voxel units and propagating in the voxel space. Specifically, according to the optical parameters of each voxel and the direction of the light ray, calculate the propagation direction and intensity change of the light ray in this voxel, and update the direction and intensity values of the light ray. For example, the refraction direction of the light ray at the voxel interface can be calculated according to Snell's law, and the absorption attenuation of the light ray in the voxel can be calculated according to the Beer-Lambert law to generate light propagation vector data. For each light ray path, mark each voxel unit passed by it as a branch node of the light ray trajectory. For example, the coordinates of each voxel on the light ray path can be stored in a linked list, and this linked list is used as the branch node information of the light ray trajectory. Preset the light propagation termination conditions, such as the light propagation distance exceeding the preset threshold, the light intensity decaying to below the preset threshold, or the light ray propagating to the model boundary, etc. Then, use the preset light propagation termination conditions to analyze the light ray path chain data generated in step S255, identify the light ray propagation paths that meet the termination conditions, and mark them as the termination state. Finally, obtain the light curing ray tracing data containing all valid light ray propagation paths and energy attenuation information.

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

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

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

[0138] Step S2523: Discretize the light propagation path according to the voxel optical interaction model and the voxel internal attenuation model, and establish an iterative calculation model for light propagation between voxels through finite element analysis to obtain an iterative light propagation equation system;

[0139] Step S2524: Calculate the light propagation operator according to the light propagation iteration equation set to obtain light propagation operator data;

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

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

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

[0143] Step S2528: Based on a preset stability index threshold, perform light field stability determination on the voxel iterative light field data through the iterative light field difference data. When the difference between adjacent two 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 = |n 1 ×cosθi ― n 2 ×cosθt|÷|n 1 ×cosθi + n 2 ×cosθt| 2 ; Rp = |n 1 ×cosθt ― n 2 ×cosθi|÷|n 1 ×cosθt + n 2 ×cosθi| 2 ; where Rs and Rp respectively represent the reflectivities of s-polarized light and p-polarized light, n1 and n2 respectively represent the refractive indices of the two media, θi represents the incident angle, and θt represents the refraction angle. 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 incident angle is 30°, the reflectivities of s-polarized light and p-polarized light can be calculated according to the Fresnel formula as 0.0012 and 0.0004 respectively. According to the absorption coefficient and scattering coefficient of each voxel, the Lambert-Beer law is used to calculate the energy attenuation degree of light when passing through the voxel. Its expression is: I = I 0×exp(―μ×d); where I represents the intensity of the light after propagating a distance d, and I 0 represents the initial intensity of the light, and μ represents the absorption coefficient of the medium. For example, when light propagates 1 mm in a voxel with an absorption coefficient of 0.1 mm-1, the light intensity can be calculated to decay 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 the light within each sub-voxel is regarded as a straight line segment. Then, using the finite element analysis method, an iterative calculation model for the propagation of light between voxels is established. For example, the optical field value of each sub-voxel can be taken as an unknown quantity, and the optical interactions such as reflection, refraction, and absorption of the light at the sub-voxel interface can be expressed in the form of a linear equation system. Finally, an iterative equation system for light propagation is obtained. For example: A×X = b; where 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 the 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, operations such as LU decomposition or Gaussian elimination can be performed on the coefficient matrix A to obtain the light propagation operator data. For example: X ―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 optical field vector X into the iterative equation system for light propagation to calculate the optical field vector X' at the next moment. Take X' as the new initial optical field vector and repeat the iterative calculation until the optical field distribution reaches a stable state. The optical field data obtained from each iterative calculation is weighted and averaged with the optical field data of the previous iteration to obtain new optical field data. For example, the following formula can be used for optical field iterative update: X new = α×X old +(1―α)×X'; where X new represents the new optical field data, and X oldLet \(E\) represent the light field data of the previous iteration, \(X'\) represent the light field data obtained from the current iteration calculation, and \(\alpha\) represent the weighting coefficient with a value range from 0 to 1 to generate voxel iterative light field data. Calculate the light intensity difference, ray direction difference, and light field energy difference for each voxel between two adjacent iterative light fields. For example, the following formula can be used to calculate the voxel light intensity difference: \(\Delta I = |I_{new}-I_{old}|\); where \(\Delta I\) represents the voxel light intensity difference, \(I_{new}\) represents the light intensity value of the voxel in the new light field data, and \(I_{old}\) represents the light intensity value of the voxel in the light field data of the previous iteration, generating iterative light field difference data, including voxel light intensity difference data, voxel ray direction difference data, and light field energy difference data. Determine whether the difference for each voxel between two adjacent iterative light fields is less than or equal to the corresponding threshold. If the differences for all voxels are less than or equal to the corresponding thresholds, it is considered that 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 until the light field reaches a stable state.

[0145] As an example of the present invention, refer to Figure 2 as shown in Figure 1 the detailed implementation step flow diagram of step S3 in

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

[0147] In the embodiment of the present invention, the energy attenuation amount of the light ray in each voxel is calculated according to the Lambert-Beer law, and the energy of each light ray 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 ray 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 this light ray is 0.5.

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

[0149] In the embodiment of the present invention, the light rays with a larger path energy attenuation ratio are given a smaller weight, and the light rays with a smaller path energy attenuation ratio are given a larger weight. For example, the following formula can be used to calculate the light ray weight: \(w = 1-\beta\times(1 - E\div E_0)\); where \(w\) represents the light ray weight, \(\beta\) represents the weight adjustment coefficient with a value range from 0 to 1, \(E\) represents the energy of the light ray when it reaches the resin surface, and \(E_0\) represents the initial energy of the light ray. Multiply the energy of all the light rays received on the surface of each voxel by the corresponding weight and then perform accumulation to obtain the cumulative light intensity energy contribution data of this voxel.

[0150] Step S33: Process the initial surface light intensity distribution data through the cumulative 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. This data reflects the light energy distribution actually received at each position on the resin surface.

[0152] Step S34: Process the resin curing state parameters according to the resin material photocuring parameters to generate resin curing state parameter data;

[0153] In the embodiment of the present invention, 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., the resin curing state parameters are processed. For example, according to the curing depth of the resin, the curing state parameters at different depths below the resin surface can be calculated, such as the degree of cure, the polymerization rate, etc., and these parameters can be calculated using the photocuring kinetic model of the resin. It is also possible to convert the surface light intensity energy distribution data 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: Obtain the photosensitizer reaction rate equation, and establish a mapping relationship between the resin curing state parameters and the photosensitizer concentration according to the photosensitizer reaction rate equation and the curing state parameter data to generate curing state parameter mapping data;

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

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

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

[0158] Step S37: Perform photosensitive concentration time series analysis on the cumulative light dose prediction sequence data by using the curing state parameter mapping data, and perform resin curing degree quantification processing to generate resin curing degree prediction curve data.

[0159] In the embodiment of the present invention, according to the cumulative light dose at each time point, the remaining photosensitizer concentration at the corresponding time point is calculated by using the photosensitizer reaction rate equation. Then, according to the remaining photosensitizer concentration and the curing state parameter mapping data, the corresponding curing state parameters, such as the curing degree, are found. For example, according to the cumulative light dose prediction sequence data, the change amount of the photosensitizer concentration on the surface of each voxel within each time window can be calculated and accumulated to obtain the photosensitizer concentration time series data of the voxel. 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 degrees at each time point are connected to generate resin curing degree prediction curve data.

[0160] As an example of the present invention, refer to Figure 3 shown, for Figure 1 the detailed implementation step flow diagram of step S4 in

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

[0162] In the 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 light guide output head for installing the beam modulation unit. A suitable beam modulation unit is selected. The beam modulation unit includes multiple lasers, an adjustable optical attenuator, a beam shaper, and a beam combiner, 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 good connection between the beam modulation unit and the light guide 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 light guide output head.

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

[0164] In an embodiment of the present invention, a set of laser excitation sequences is preset, for example, a laser pulse sequence including different wavelengths, different pulse widths, and different repetition frequencies. For example, a set of laser pulse sequences including two wavelengths of 635 nm and 850 nm can be set, the laser pulse width of each wavelength is 10 ns, and the repetition frequency is 10 kHz. 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, using the photoacoustic detector built in the intelligent dental light-curing machine, such as a piezoelectric sensor, real-time photoacoustic detection is performed on the target area to collect photoacoustic signals. The photoacoustic signal data collected after each laser pulse emission is associated with the corresponding laser parameter information to generate multi-wavelength laser feedback signal data.

[0165] Step S43: Perform 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, photoacoustic image fusion processing is performed on the multi-wavelength laser feedback signal data generated in step S42. For example, algorithms such as the delay superposition algorithm or the back-projection algorithm can be used to reconstruct the photoacoustic signal data of different wavelengths into corresponding photoacoustic images, and the photoacoustic images of different wavelengths are fused to generate real-time dynamic tomography image data. For example, the photoacoustic image generated by 635-nm laser excitation and the photoacoustic image generated by 850-nm laser excitation can be superimposed to generate a fused photoacoustic image. The real-time dynamic tomography image data reflects the change of the light absorption characteristics of the target area over time.

[0167] Step S44: Perform resin curing eigenvector processing on the multi-parameter eigenvalue 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 tomography image data generated in step S43 is imported into a computer, and the multi-parameter eigenvalue corresponding to each pixel point is extracted, such as the photoacoustic signal amplitude, the photoacoustic signal frequency, the photoacoustic signal attenuation coefficient, etc. The multi-parameter eigenvalues of each pixel point are combined into a vector to generate curing parameter vector space data. For example, the 635-nm photoacoustic signal amplitude, the 850-nm photoacoustic signal amplitude, and the photoacoustic signal attenuation coefficient of each pixel point 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 the embodiment of the present invention, the curing degree is calculated based on the curing parameter vector space data generated according to step S44. For example, a relationship model between the curing parameter vector and the resin curing degree can be established by using machine learning algorithms such as support vector machines or neural networks, and the model is used to predict the resin curing degree of each pixel point. For example, by using a trained support vector machine model, the curing parameter vector of each pixel point is input into the model, and the corresponding resin curing degree value is output. The resin curing degree values of each pixel point are stored to generate resin curing degree data.

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

[0172] In the embodiment of the present invention, the real-time dynamic tomography image data generated in step S43 is subjected to curing parameter clustering processing through the resin curing degree data generated in step S45. For example, algorithms such as the K-means algorithm or the hierarchical clustering algorithm can be used to cluster pixel points with similar curing degrees together. Then, boundary recognition is performed on the clustering result. For example, algorithms such as edge detection algorithms or region growing algorithms are used to identify the boundaries between different curing degree regions to generate curing area boundary data.

[0173] Step S47: Perform curing state label marking based on the curing area boundary data, and extract the curing degree of the key area; generate resin actual curing degree data.

[0174] In the embodiment of the present invention, the real-time dynamic tomography image data is subjected to curing state label marking according to the curing area boundary data generated in step S46. For example, pixel points belonging to the same curing degree region can be marked with the same label. For example, regions with a higher curing degree are marked as "cured", and regions with a lower curing degree are marked as "uncured". Then, according to the preset key areas, such as the bottom or edge of a dental caries, the curing degree values of the key areas are extracted to generate resin actual curing degree data. For example, the average curing degree value of all pixel points within the key area can be calculated as the resin actual curing degree of this area.

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

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

[0177] Step S432: Extract the target photoacoustic component from the multi-wavelength laser feedback signal data through the photoacoustic signal denoised wavelet coefficients to generate target photoacoustic component data;

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

[0179] Step S434: Perform pixel photoacoustic signal spectrum analysis on the two-dimensional resin tomographic image sequence data to generate photoacoustic spectrum feature map data;

[0180] Step S435: Perform multi-dimensional resin photoacoustic feature extraction based on the photoacoustic spectrum feature map data to obtain a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively;

[0181] Step S436: Based on the two-dimensional resin tomographic image sequence data, estimate the pixel photoacoustic velocity of the photoacoustic spectrum feature map data to generate a photoacoustic velocity map;

[0182] Step S437: Use the photoacoustic velocity map, the photoacoustic absorption coefficient map, and the photoacoustic scattering coefficient map to perform multi-parameter eigenvalue 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 band-pass filter is used to limit the frequency range of the photoacoustic signal between 1 MHz and 10 MHz. Then, wavelet coefficient denoising processing is performed on the preprocessed photoacoustic signal. For example, wavelet transform can be used to decompose the photoacoustic signal into sub-bands of different frequencies, and threshold denoising processing is performed on each sub-band 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 characteristics of the resin material, wavelet coefficients in a specific frequency range are selected as the target photoacoustic components. For example, wavelet coefficients in the frequency range between 2 MHz and 5 MHz can be selected as the target photoacoustic components of the resin material. The wavelet coefficients of the target photoacoustic components are reconstructed to generate target photoacoustic component data. The target photoacoustic component data received by each photoacoustic detector is 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 tomography image sequence data. The two-dimensional resin tomography image sequence data reflects the change of the light absorption characteristics of the target area over time. Fourier transform is performed on the photoacoustic signal of each pixel point to obtain the photoacoustic signal spectrum of the pixel point. The photoacoustic signal spectra of each pixel point are plotted 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, the photoacoustic absorption coefficient and the photoacoustic scattering coefficient of each pixel point are calculated. For example, the photoacoustic diffusion equation can be used to correlate 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 an inversion algorithm. The photoacoustic absorption coefficients and the photoacoustic scattering coefficients of each pixel point are stored to generate a photoacoustic absorption coefficient map and a photoacoustic scattering coefficient map respectively. Using the flight time of the photoacoustic signal and the spatial position of the photoacoustic detector, the photoacoustic velocity of each pixel point is calculated. 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 according to the time delay and the distance between the photoacoustic detectors. The photoacoustic velocity, the photoacoustic absorption coefficient, and the photoacoustic scattering coefficient of each pixel point are combined into a vector as the multi-parameter eigenvalue of the pixel point. Then, resin curing feature weight fusion is performed on the multi-parameter eigenvalue. For example, according to the light curing characteristics of the resin material, different weights are assigned to different eigenvalues, and the weighted eigenvalues are fused to generate real-time dynamic tomography image data. For example, a larger weight can be assigned to the photoacoustic absorption coefficient, a smaller weight can be assigned to the photoacoustic scattering coefficient, and the weighted photoacoustic absorption coefficient and the photoacoustic scattering coefficient are linearly combined to generate real-time dynamic tomography image data.

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

[0185] Step S51: Align the actual resin curing degree data along the time axis based on the resin curing degree prediction curve data, and process the actual curing degree curve to generate actual curing degree curve data;

[0186] Step S52: Calculate the curing degree error of the actual curing degree curve data using the resin curing degree prediction curve data to generate curing degree error curve data;

[0187] Step S53: Classify and give early warnings for the deviation degree of the curing degree error curve data, and match the adjustment parameters through the preset curing machine parameter adjustment rules to generate light source parameter adjustment values;

[0188] Step S54: The staff adjusts the light source parameters of the intelligent dental light curing machine according to the light source parameter adjustment values to generate light source regulation state data;

[0189] Step S55: Determine the light curing termination state based on the light source regulation state data. When the light curing terminates, use the beam modulation unit to collect the termination photoacoustic signal and evaluate the curing quality to generate light curing quality evaluation data.

[0190] In an embodiment of the present invention, according to the time point when the actual curing degree data of the resin starts to rise, the time axis of the predicted curing degree curve is translated so that it is aligned with the time axis of the actual curing degree curve. For example, if the actual curing degree data starts to rise at the 5th second, the time axis of the predicted curing degree curve is translated 5 seconds to the right. Then, curve smoothing processing is performed on the actual curing degree data after the time axis alignment, 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. Calculate the difference between the predicted curing degree value and the actual curing degree value corresponding to each time point as the curing degree error at that time point. For example, according to a preset error threshold, the curing degree error can be divided into different levels, such as slight deviation, medium deviation, and severe deviation, etc. For example, an error less than 5% can be classified as a slight deviation, an error between 5% and 10% can be classified as a medium deviation, and an error greater than 10% can be classified as a severe deviation. Then, according to the preset curing machine parameter adjustment rules, such as the light intensity adjustment rule, the light exposure time adjustment rule, etc., parameter matching is performed. 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 regulated. 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 parameters such as the light intensity and light exposure time of the light source. For example, if the adjusted value of the light source parameter is to increase the light intensity by 10%, the staff can increase the light intensity by 10% through the control panel or computer software. Store the status information after the light source parameter adjustment to generate light source regulation status data. For example, according to a preset light curing time or resin curing degree threshold, it can be determined whether the light curing reaches the termination state. For example, if the preset light curing time is 60 seconds, when the light curing time reaches 60 seconds, it is determined that the light curing terminates. When the light curing terminates, the termination photoacoustic signal is collected using the beam modulation unit installed in step S41. For example, a short laser pulse can be emitted using the beam modulation unit, and the photoacoustic signal is collected using a photoacoustic detector. Then, the collected photoacoustic signal is analyzed, such as calculating characteristic parameters such as the photoacoustic signal amplitude and photoacoustic signal frequency, and according to a pre-established light curing quality evaluation model, such as a model based on a support vector machine or a neural network, the resin curing quality is evaluated to generate light curing 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 includes:

[0192] A multi - perspective light field acquisition module is used to irradiate a target area light source on a target dental light - curing area by using an intelligent dental light - curing machine, generating multi - perspective light intensity signal data; performing light field matrix processing on the multi - perspective light intensity signal data to generate multi - perspective light field matrix data;

[0193] A curing light intensity analysis module is used to obtain resin curing material parameters; performing light field voxel space processing on the target dental light - curing area through the multi - perspective light field matrix data to obtain light field voxel space data; analyzing the light propagation path of the light field voxel space data by using the resin curing material parameters to generate light - curing ray tracing data; calculating the resin surface light intensity value according to the light - curing ray tracing data to generate initial surface light intensity distribution data;

[0194] A resin curing prediction module is used to perform resin surface light intensity distribution processing on the initial surface light intensity distribution data through the light - curing ray tracing data to generate surface light intensity energy distribution data; predicting the resin photosensitive curing by using the resin curing material parameters for the surface light intensity energy distribution data to generate resin curing degree prediction curve data;

[0195] A curing degree real - time monitoring module is used to perform real - time photoacoustic detection on the target dental light - curing area by using an intelligent dental light - curing machine to generate multi - wavelength laser feedback signal data; performing resin curing eigenvector processing on the multi - wavelength laser feedback signal data and extracting the curing degree of the key area; generating resin actual curing degree data;

[0196] A curing quality evaluation module is used to calculate the curing degree error of the resin actual curing degree data by using the resin curing degree prediction curve data to generate curing degree error curve data; regulating the light source parameters of the intelligent dental light - curing machine through the curing degree error curve data and performing termination light - curing quality evaluation to generate light - curing quality evaluation data.

[0197] The beneficial effects of this application are as follows. By deploying a microlens array on the irradiation head of the light curing machine and combining multi-angle light intensity signal acquisition and light field matrix construction, precise capture of light in the target area and three-dimensional reconstruction are achieved, and complete light field data including 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 in the resin, and the light intensity energy distribution at various positions on the resin surface and inside is accurately calculated. The beam modulation unit is integrated into the light curing machine, and multi-wavelength laser pulses are emitted to the target area using a preset laser excitation sequence. By real-time collecting photoacoustic signals and performing photoacoustic image reconstruction, spectral 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 tomographic image is constructed. Then, a relationship model between the degree of resin curing and multi-parameter photoacoustic features is established using machine learning algorithms to achieve real-time quantitative evaluation of the actual degree of resin curing. The resin curing degree prediction curve and the actual curing degree curve are aligned on the time axis and the error is calculated, and early warnings are classified according to the degree of error. Then, according to the preset curing machine parameter adjustment rules, such as the light intensity adjustment rule, the light irradiation time adjustment rule, 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 collect the termination photoacoustic signal and evaluate the resin curing quality according to the pre-established light curing quality evaluation model to generate light curing quality evaluation data. Thus, closed-loop control of the resin curing process is achieved, ensuring that the resin achieves the expected curing effect. The problem that traditional light curing methods are difficult to accurately control the curing effect is solved, and the accuracy and efficiency of dental restoration treatment are improved.

[0198] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be embraced within the present invention.

[0199] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can 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 these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed 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; 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 according to the light curing ray tracing data to generate initial surface light intensity distribution data; wherein, step S2 is specifically as follows: 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; 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 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.

4. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 3, 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.

5. The real-time monitoring method of an intelligent medical dental light curing machine according to claim 1, 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.

6. The real-time monitoring method of 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.

7. 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.

8. 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.

9. 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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