A dental unit testing method and system

Abstract

The application relates to the technical field of fault diagnosis of dental comprehensive treatment machines, in particular to a dental comprehensive treatment machine detection method and system, which comprises the following steps: executing a load sequence simulating clinical continuous operation of a dental comprehensive treatment machine; synchronously collecting waterway system state data and light-curing lamp light quality data; performing time sequence correlation analysis based on the waterway system state data and the light-curing lamp light quality data, identifying the time sequence and time interval between waterway system state changes and light-curing lamp light quality degradation; judging the degradation source of performance degradation according to the time sequence and the time interval; and outputting a diagnosis report according to the time sequence, the time interval and the degradation source, wherein the diagnosis report indicates the performance degradation condition and the fault source of the dental comprehensive treatment machine. The method has the advantages that the correlation between waterway system state changes and light-curing lamp light quality degradation of the dental comprehensive treatment machine under the load of clinical continuous operation can be identified, so that the degradation source of performance degradation can be judged.

Description

Technical Field

[0001] This application relates to the field of fault diagnosis technology for dental comprehensive treatment machines, and more specifically, to a testing method and system for dental comprehensive treatment machines. Background Technology

[0002] In the field of dental treatment, dental treatment units are core equipment, and their stability and accuracy are crucial to clinical treatment outcomes. However, in actual clinical applications, dental treatment units that have been in use for a long time often exhibit subtle and gradual performance degradation. This degradation does not stem from the complete failure of a single component, but rather from the incoordination of multiple subsystems within the device under actual workload conditions, leading to a drift in the overall system function. For example, the light-curing function is an important component of dental treatment units, and even slight degradation in its performance can lead to incomplete polymerization of dental materials, resulting in long-term clinical problems with restorations, such as marginal discoloration, secondary caries, or localized fracture. These problems often only become apparent some time after the procedure, making it difficult for clinicians to directly trace the issue back to the device's performance.

[0003] Traditional testing methods for dental integrated treatment units typically focus on independent, static performance tests of each functional unit. For example, technicians might use a handheld power meter to measure the peak light intensity of the curing lamp or a conventional thermometer to measure the water outlet temperature. While these individual test results may show that the performance of each component is still within acceptable limits, the dynamic interactions between its internal subsystems and long-term performance drift under actual continuous clinical operating loads are difficult to effectively identify.

[0004] The challenge of existing technologies lies in the complex interactions between the water system and multiple subsystems, such as the light curing lamp, in actual clinical operation of dental integrated treatment units. When the water system experiences nonlinear temperature drift over time that is difficult to identify through static testing, this drift affects the cooling efficiency of the light curing lamp unit, leading to aging of the light-emitting elements, causing non-uniform attenuation of light intensity and changes in light color composition, ultimately resulting in incomplete polymerization of dental materials and delayed clinical failure. Traditional approaches based on "individual testing" and "static testing" cannot pinpoint problems in this scenario because they cannot simulate the real working state of the equipment during actual clinical operation, where multiple systems interact and influence each other. Consequently, they cannot capture these functional defects that accumulate over time during system collaboration.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for testing dental comprehensive treatment machines, which has the advantage of being able to effectively identify the correlation between changes in the state of the water system and the degradation of the light quality of the curing lamp under continuous clinical operation load, thereby accurately determining the source of performance degradation.

[0007] This application provides a method for testing a dental integrated treatment machine, the method comprising:

[0008] A load sequence is executed to simulate continuous clinical operation of a dental comprehensive treatment machine. The load sequence is used to induce performance changes in multiple subsystems within the dental comprehensive treatment machine under workload conditions.

[0009] During the execution of the load sequence, water system status data and curing lamp light quality data are collected simultaneously. Water system status data includes instantaneous water temperature and water flow rate, while curing lamp light quality data includes spectral peak wavelength, spectral width, and energy distribution map inside the light spot.

[0010] Based on the water system status data and the light curing lamp quality data, a time-series correlation analysis was performed to identify the temporal sequence and time interval between changes in the water system status and degradation of the light curing lamp quality.

[0011] Determine the source of performance degradation based on the chronological order and time interval;

[0012] The diagnostic report is generated based on the chronological order, time interval, and source of degradation. The diagnostic report indicates the performance degradation of the dental comprehensive treatment machine and the source of the malfunction.

[0013] Optionally, based on the water system status data and the curing lamp light quality data, a time-series correlation analysis is performed to identify the temporal sequence and time interval between changes in the water system status and degradation of the curing lamp light quality, including:

[0014] The instantaneous temperature change rate of the water channel and the degradation rate of the light curing lamp quality are obtained. Based on the instantaneous temperature change rate of the water channel and the degradation rate of the light curing lamp quality, the heat transfer efficiency index is calculated.

[0015] Acquire environmental parameters that characterize the external environment, including ambient temperature;

[0016] Based on the heat transfer efficiency index and ambient temperature, an adjustment factor is constructed to set the time delay adjustment interval for time series correlation analysis.

[0017] Within the time delay adjustment range, identify the temporal sequence and time interval between changes in the water system status and degradation of the light curing lamp quality;

[0018] Based on the chronological order and time interval, determine the source of performance degradation, including: whether the performance degradation is caused by changes in the state of the water system based on whether the chronological order and time interval consistently fall within the time delay adjustment range.

[0019] Optionally, within the time delay adjustment interval, the temporal sequence and time interval between changes in the water system status and degradation of the light curing lamp quality are identified, including:

[0020] Identify changes in the state of the water system, determine whether the instantaneous temperature or flow rate of the water system continuously exceeds a preset change threshold, and whether the duration exceeds a preset duration threshold, and determine the event point of the change in the state of the water system.

[0021] To identify the degradation of light curing lamp light quality, determine whether the spectral peak wavelength has shifted, whether the spectral width has broadened, or whether the uniformity of energy distribution within the light spot has decreased, and determine whether the time of change of light curing lamp light quality degradation has continued to exceed the corresponding threshold duration, thus determining the light curing lamp light quality degradation event point.

[0022] Based on the event points of water system state change and light curing lamp quality degradation, the temporal sequence and time interval between the water system state change and the light curing lamp quality degradation are identified within the time delay adjustment interval.

[0023] Optionally, determining the source of performance degradation based on chronological order and time intervals also includes:

[0024] The rate of decrease in uniformity of spot energy distribution, the rate of shift in spectral peak wavelength, and the region of uneven spot energy distribution corresponding to the degradation of the light curing lamp's light quality were obtained.

[0025] The degradation event mode of the light curing lamp light quality was determined based on the rate of decrease in the uniformity of the light spot energy distribution, the rate of shift in the wavelength of the spectral peak, and the region of uneven light spot energy distribution.

[0026] Based on the event pattern of light curing lamp quality degradation, and combined with whether the time sequence and time interval consistently fall within the time delay adjustment range, it is determined whether the source of performance degradation is caused by the quality degradation of the light curing lamp.

[0027] Optionally, based on the rate of decrease in the uniformity of the light spot energy distribution, the rate of shift in the wavelength of the spectral peak, and the region of uneven light spot energy distribution, the degradation event mode of the light curing lamp quality is determined, including:

[0028] The anomaly degree is calculated for the rate of decrease in the uniformity of the light spot energy distribution and the rate of shift in the wavelength of the spectral peak, respectively. The anomaly degree calculation is based on the corresponding preset threshold and the corresponding deviation amplitude.

[0029] By comparing the degree of anomaly in the rate of decrease in the uniformity of the light spot energy distribution with the degree of anomaly in the rate of shift in the wavelength of the spectral peak, the one with the greater degree of anomaly is identified as the dominant indicator of light degradation; the geometric characteristics of the uneven region of the light spot energy distribution are analyzed, including shape, size and location.

[0030] Based on the dominant indicators and geometric characteristics of light degradation, the event mode of light quality degradation of curing lamps was determined.

[0031] Optionally, determining the degradation event mode of curing lamp light quality based on the dominant light degradation index and geometric characteristics also includes:

[0032] Obtain preset light curing lamp quality degradation mode rules, which include the range of light degradation dominant indicators and the range of geometric features corresponding to multiple light curing lamp quality degradation event modes;

[0033] The dominant indicators and geometric features of light degradation are matched with the rules of light quality degradation mode of curing lamps to obtain the matching results;

[0034] When multiple matching results exist, a controlled disturbance is performed, which may include adjusting the instantaneous temperature of the water circuit or adjusting the output power of the light curing lamp.

[0035] During the controlled disturbance execution process, the dynamic changes in the quality data of the light curing lamp are collected;

[0036] Based on the dynamic changes in light quality data, response features of light quality degradation event patterns in curing lamps are extracted;

[0037] The response curves of the curing lamp light quality degradation event modes contained in the multiple matching results are matched with the response features respectively, and the matching result with the highest matching degree is determined as the final curing lamp light quality degradation event mode.

[0038] Optionally, based on the dynamic changes in light quality data, response features of light quality degradation event patterns in curing lamps are extracted, including:

[0039] Determine the baseline state of the UV curing lamp light quality data before the start of controlled disturbance;

[0040] Identify the point in time when the quality data of the curing lamp light starts to deviate from the baseline state and continues to exceed a preset deviation threshold, and determine the starting point of the transient response;

[0041] Identify the time point at which the quality data of the curing lamp light reaches and remains within a preset stable range after the transient response start point, and determine the stable end point;

[0042] Calculate the response amplitude based on the baseline state, the transient response start point, and the steady-state end point;

[0043] Calculate the transient response time based on the start time of the controlled disturbance and the starting point of the transient response;

[0044] The settling time is calculated based on the transient response start point and the stable end point; the response amplitude, transient response time, and settling time are used as response characteristics of the light curing lamp quality degradation event mode.

[0045] Optionally, a diagnostic report can be output based on the chronological order, time intervals, and source of degradation, including:

[0046] The degradation event pattern of light curing lamp quality degradation is matched with the preset performance degradation pattern classification rules to determine the degradation type of the current performance degradation of the dental comprehensive treatment machine;

[0047] Based on the degradation type and degradation source, a fault source identifier is generated, which indicates the abnormal performance of the corresponding subsystem of the dental comprehensive treatment machine; the degradation type, degradation source, and fault source identifier are written into the diagnostic report, and the diagnostic report is output.

[0048] Optionally, during the execution of the load sequence, water system status data and light curing lamp quality data are collected synchronously, including:

[0049] A unified sampling clock reference is set, and based on the sampling clock reference, the status data of the water system and the quality data of the light curing lamp are synchronously collected;

[0050] The rate of change of instantaneous temperature and the rate of change of water flow rate in the water path are obtained respectively, and the dynamic sampling frequency of the water path system status data is adjusted based on the rate of change of instantaneous temperature and the rate of change of water flow rate in the water path.

[0051] Set a fixed high-frequency sampling frequency for the spectral peak wavelength, spectral width, and energy distribution map inside the light spot, and perform data integrity verification within the specified sampling period;

[0052] A timestamp is generated and the corresponding dynamic sampling frequency and fixed high-frequency sampling frequency are recorded in each sampling period to construct a time synchronization index table. Based on the time synchronization index table, data timeline alignment and dynamic registration are achieved during time series correlation analysis.

[0053] Optionally, a dental integrated treatment machine detection system includes:

[0054] The load execution module is used to execute load sequences that simulate continuous clinical operation of a dental comprehensive treatment machine. The load sequences are used to induce performance changes in multiple subsystems within the dental comprehensive treatment machine under workload conditions.

[0055] The synchronous acquisition module is used to synchronously acquire water system status data and curing lamp light quality data during the execution of the load sequence. The water system status data includes the instantaneous temperature and flow rate of the water system, and the curing lamp light quality data includes the peak wavelength, spectral width, and energy distribution map inside the light spot.

[0056] The correlation analysis module is used to perform time-series correlation analysis based on water system status data and curing lamp quality data to identify the temporal sequence and time interval between changes in water system status and degradation of curing lamp quality.

[0057] The degradation source determination module is used to determine the source of performance degradation based on the chronological order and time interval.

[0058] The output module is used to output diagnostic reports based on the time sequence, time interval, and source of degradation. The diagnostic reports indicate the performance degradation of the dental comprehensive treatment machine and the source of the fault.

[0059] As can be seen from the above, the dental comprehensive treatment machine testing method and system provided in this application simulates continuous clinical operating load, synchronously collects data from the water system and curing lamp, and performs time-series correlation analysis to identify the source of performance degradation. This effectively solves the problem of difficulty in identifying the hidden performance degradation caused by the synergistic effect of multiple systems in dental comprehensive treatment machines under actual working load in the prior art. It has the advantage of being able to effectively identify the time-series correlation between changes in the state of the water system and the degradation of the curing lamp light quality under continuous clinical operating load, thereby accurately determining the source of performance degradation. It overcomes the shortcomings of traditional static testing, which cannot capture the hidden degradation caused by the dynamic interaction between systems, and provides a scientific basis for the early fault diagnosis and maintenance of dental comprehensive treatment machines. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a dental comprehensive treatment machine testing method provided in this application.

[0061] Figure 2 A flowchart of a dental comprehensive treatment machine testing system provided in this application.

[0062] In the diagram: 1. Load execution module; 2. Synchronous acquisition module; 3. Correlation analysis module; 4. Degradation source judgment module; 5. Output module. Detailed Implementation

[0063] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0064] Reference Figure 1 In response, this application proposes a method for testing a dental comprehensive treatment machine, the method comprising:

[0065] S1000: Executes a load sequence that simulates continuous clinical operation of a dental comprehensive treatment machine. The load sequence is used to induce performance changes in multiple subsystems within the dental comprehensive treatment machine under working load conditions.

[0066] S2000: During the execution of the load sequence, the water system status data and the light curing lamp quality data are collected simultaneously. The water system status data includes the instantaneous temperature and flow rate of the water system, and the light curing lamp quality data includes the peak wavelength, spectral width, and energy distribution map inside the light spot.

[0067] S3000: Based on the water system status data and the light curing lamp quality data, perform time-series correlation analysis to identify the temporal sequence and time interval between changes in the water system status and degradation of the light curing lamp quality.

[0068] S4000: Determine the source of performance degradation based on the chronological order and time interval;

[0069] S5000: Outputs diagnostic reports based on time sequence, time interval, and source of degradation. The diagnostic reports indicate the performance degradation of the dental comprehensive treatment machine and the source of the malfunction.

[0070] The load sequence refers to a series of preset working modes and intensity changes that simulate the continuous operation of a dental treatment machine during actual clinical diagnosis and treatment. This can be simulated using pre-programmed automated test scripts, manual simulation of operating procedures, or by combining actual case data. Water system status data refers to real-time or near-real-time measurements reflecting the operating status of the dental treatment machine's internal water system, specifically the instantaneous temperature and flow rate of the water system. This data can be collected using high-precision temperature and flow sensors. Curing lamp light quality data refers to key optical parameters characterizing the output light performance of the curing lamp unit in the dental treatment machine. Specifically, this includes the peak wavelength, spectral width, and energy distribution within the light spot. This data can be collected and analyzed using a spectrometer, light spot analyzer, or high-resolution CCD camera combined with specialized software. Temporal correlation analysis is a data analysis technique used to explore whether there are temporal dependencies or causal relationships between different time series data. It can be implemented using cross-correlation functions, Granger causality tests, or dynamic time warping algorithms.

[0071] In temporal sequence analysis, the order of events and time interval refer to the temporal relationship between two or more events. The order of events indicates which event occurred first and which occurred later, while the time interval is the time difference between the occurrences of two events. It can be determined using timestamp comparison, event log analysis, or a sliding window-based synchronization detection method. A diagnostic report is a structured document or electronic output containing a detailed assessment of the performance degradation of a dental treatment unit and a clear indication of the source of the malfunction. It can be generated using predefined report templates, data visualization charts, or text descriptions.

[0072] This application simulates the continuous clinical operation of a dental integrated treatment machine by executing a load sequence, and simultaneously collecting water system status data and curing lamp light quality data during the process. Then, it performs time-series correlation analysis on these data to identify the temporal sequence and time interval between changes in the water system status and degradation of curing lamp light quality. This allows for accurate determination of the source of performance degradation, effectively detecting curing lamp performance degradation caused by the interaction of multiple internal subsystems under actual workload of a dental integrated treatment machine, and providing accurate fault diagnosis.

[0073] In some preferred embodiments, an automated testing platform is used to execute a load sequence simulating continuous clinical operation of a dental treatment machine. This platform uses a robotic arm or solenoid valve to precisely simulate the dentist's continuous operation of components such as the treatment machine handle, foot switch, and water / air gun. For example, it simulates three consecutive light curing operations, each lasting 20 seconds with a 10-second interval, while simultaneously simulating the water flushing function. During the execution of the load sequence, water system status data and light curing lamp light quality data are simultaneously acquired. Instantaneous water system temperature can be acquired by installing high-precision thermistors or platinum resistance temperature sensors at key locations in the water system (e.g., heater outlet, light curing lamp cooling water inlet), while the water flow rate is monitored in real time using a micro turbine flow meter or ultrasonic flow meter. For light curing lamp light quality data, the spectral peak wavelength and spectral width can be obtained through real-time spectral analysis using a spectrometer integrated near the light outlet of the light curing lamp. The energy distribution map inside the light spot can be imaged and analyzed using a high-resolution CCD camera or CMOS image sensor combined with beam analysis software. All sensors and acquisition devices are connected to a unified data acquisition unit, which uses a high-precision synchronous clock chip to ensure accurate timestamp alignment of all data points, for example, synchronous acquisition with a minimum sampling interval of 100 milliseconds. Based on the acquired water system status data and curing lamp light quality data, time-series correlation analysis is performed. This can be achieved through a data processing server that runs specialized time-series analysis algorithms, such as sliding window cross-correlation analysis, to calculate the correlation coefficient between the instantaneous temperature change curve of the water system and the peak wavelength shift curve of the curing lamp's spectrum, and to identify the time delay with the strongest correlation. For example, if the instantaneous temperature of the water system rises by more than 0.5 degrees Celsius, and the peak wavelength of the curing lamp's spectrum shifts by more than 0.5 nanometers within the following 30 to 90 seconds, a correlation is considered to exist. Based on the identified time sequence and time interval, the source of performance degradation is determined. For example, if the analysis consistently shows that changes in the water system status (such as increased water temperature) always precede the degradation of the LED curing lamp's light quality (such as spectral shift), and the time intervals consistently fall within a preset physical response range (e.g., the typical time for cooling water temperature changes to be transmitted to the LED chip and cause spectral changes), then the system determines that the primary source of performance degradation is the water system. Conversely, if the degradation of the LED curing lamp's light quality precedes changes in the water system, or if there is no clear temporal correlation between the two, it may be determined that the degradation is due to the aging of the LED curing lamp itself. Finally, a diagnostic report is output based on the chronological order, time intervals, and source of degradation. The diagnostic report can be generated as a PDF document or displayed on the user interface.The report will clearly state "spectral shift of the curing lamp, possible cause: decreased cooling efficiency of the water system", and provide specific data charts, such as the superimposed graph of water temperature change curve and spectral shift curve, as well as the time correlation analysis results of the two, so as to provide maintenance personnel with intuitive and accurate fault location information.

[0074] By implementing the above technical solution, this application can effectively solve the problem of progressive performance degradation caused by the interaction of multiple subsystems inside a dental treatment machine under actual workload, which is difficult to identify by traditional detection methods.

[0075] In another embodiment of this application, S3000 is further proposed to include:

[0076] S3100: Obtain the instantaneous temperature change rate of the water circuit and the degradation rate of the light curing lamp quality; calculate the heat transfer efficiency index based on the instantaneous temperature change rate of the water circuit and the degradation rate of the light curing lamp quality.

[0077] S3200: Acquires environmental parameters characterizing the external environment, including ambient temperature;

[0078] S3300: Based on the heat transfer efficiency index and ambient temperature, an adjustment factor is constructed to set the time delay adjustment range for time series correlation analysis;

[0079] S3400: Within the time delay adjustment range, identify the temporal sequence and time interval between changes in the state of the water system and the degradation of the quality of the light curing lamp.

[0080] S4000: Based on the time sequence and time interval, determine the source of performance degradation. S4100: Based on whether the time sequence and time interval continuously and stably fall within the time delay adjustment range, determine whether the source of performance degradation is caused by changes in the state of the water system.

[0081] Among them, the heat transfer efficiency index refers to the parameter that quantifies the effectiveness of heat exchange between the water circuit system and the curing lamp. Specifically, it can be calculated by the ratio, difference, or more complex functional relationship between the instantaneous temperature change rate of the water circuit and the degradation rate of the curing lamp light quality. Its purpose is to assess the degree of influence of changes in the state of the water circuit system on the thermal state of the curing lamp.

[0082] Environmental parameters refer to physical quantities that characterize the external operating environment of a dental treatment machine. Specifically, they can include ambient temperature, ambient humidity, atmospheric pressure, etc. The purpose is to comprehensively consider the influence of external factors on the internal thermodynamic processes of the equipment.

[0083] The adjustment factor is a coefficient or function that dynamically corrects the time delay range based on the heat transfer efficiency index and ambient temperature. Specifically, it can be calculated through a pre-established physical model, empirical formula, or regression model trained based on historical data.

[0084] The time delay adjustment interval refers to the dynamic range used to limit the time interval between changes in the state of the water system and the degradation of the light quality of the curing lamp during time-series correlation analysis. Specifically, it can be a time window determined by an adjustment factor.

[0085] The proposed dental integrated treatment machine testing method incorporates a modeling and correction mechanism for heat transfer effects and environmental factors, focusing on two key aspects: time-series correlation analysis and the determination of the source of performance degradation. Specifically, the system first acquires the instantaneous temperature change rate of the water circuit and the degradation rate of the curing lamp's light quality, and calculates a heat transfer efficiency index to quantify the impact of water circuit system state changes on the curing lamp's performance. Simultaneously, the system acquires environmental parameters, particularly ambient temperature, to reflect its modulating effect on the heat transfer process. Based on this, the system constructs an adjustment factor based on the heat transfer efficiency index and ambient temperature, dynamically setting the time delay adjustment interval used for time-series correlation analysis. This adapts to the fluctuations in physical thermal delay and avoids misjudgments of causal relationships due to fixed time window settings. Within this time delay adjustment interval, the system identifies the temporal sequence and time interval between changes in the water circuit system state and the degradation of the curing lamp's light quality. Only when the temporal relationship conforms to the dynamic interval constraint is a potential causal relationship determined. Finally, the degradation source determination step uses whether the temporal relationship consistently and stably falls within the dynamic interval as a criterion to identify whether performance degradation is caused by changes in the water circuit system state. The overall solution significantly improves the accuracy of time series analysis and the reliability of diagnostic results by modeling thermal coupling relationships and environmental influences.

[0086] In another embodiment of this application, step S3400 is further proposed: within the time delay adjustment interval, the step of identifying the temporal sequence and time interval between the change in the state of the water system and the degradation of the quality of the light curing lamp includes:

[0087] S3410: Identify changes in the state of the water system, determine whether the instantaneous temperature or flow rate of the water system continuously exceeds a preset change threshold, and whether the duration exceeds a preset duration threshold, and determine the event point of the change in the state of the water system.

[0088] S3420: Identify the degradation of curing lamp light quality, determine whether the spectral peak wavelength has shifted, whether the spectral width has broadened, or whether the uniformity of energy distribution inside the light spot has decreased, and determine whether the time of change of curing lamp light quality degradation has continued to exceed the corresponding threshold duration, and determine the event point of curing lamp light quality degradation.

[0089] S3430: Based on the event points of water system state change and light curing lamp quality degradation, identify the temporal sequence and time interval between the water system state change and the light curing lamp quality degradation within the time delay adjustment interval.

[0090] Among them, the preset change threshold refers to the critical value used to determine whether a significant change has occurred when monitoring the instantaneous temperature or flow rate of the water circuit. Specifically, it can be set according to the design parameters, normal operating range, and historical performance data of the dental comprehensive treatment machine. The preset duration threshold refers to the shortest time required for the change in the instantaneous temperature or flow rate of the water circuit to exceed the preset change threshold. Specifically, it can be set according to the system response characteristics and fault development patterns. The water circuit system state change event point refers to the start time or confirmation time point of the state change recorded by the system when the instantaneous temperature or flow rate of the water circuit continuously exceeds the preset change threshold and the duration exceeds the preset duration threshold. In addition, the corresponding threshold duration refers to the shortest time required to determine whether the three types of light curing lamp light quality degradation manifestations—spectral peak wavelength shift, spectral width broadening, or decrease in the uniformity of energy distribution within the light spot—have continued to exceed their respective degradation thresholds. The specific time can be set according to the characteristics and development speed of different degradation modes. The light curing lamp light quality degradation event point refers to the start time or confirmation time point of the light quality degradation phenomenon recorded by the system when the spectral peak wavelength shift, spectral width broadening, or decrease in the uniformity of energy distribution within the light spot continues to exceed its corresponding threshold duration.

[0091] The solution proposed in this application improves the accuracy of time-series correlation analysis by precisely identifying event points of changes in the water system state and event points of degradation in the light curing lamp quality. In a preferred embodiment, the detection method for the dental integrated treatment machine specifically includes the following steps: First, the system sets a preset threshold for the instantaneous temperature change of the water system to ±2℃, a preset threshold for the water flow rate change to ±0.5 ml / s, and a preset duration threshold for the water system state change to 30 seconds. When the deviation of the instantaneous temperature or water flow rate of the water system continuously exceeds its respective threshold and lasts for 30 seconds, it can be identified as a water system state change event point. The data acquisition and processing unit can monitor the above parameters in real time and apply a moving average algorithm or an exponentially weighted moving average algorithm to smooth the data. By comparing with the baseline value or historical average value, it identifies whether the change threshold is exceeded and the duration requirement is met, and finally records the event point timestamp. To address the degradation of curing lamp light quality, the system sets thresholds for spectral peak wavelength shift of ±5 nm, spectral width broadening of 10%, and reduction in energy distribution uniformity within the light spot of 20%. Corresponding threshold durations are set at 15 seconds, 20 seconds, and 25 seconds, respectively. Using a miniature spectrometer and light spot analyzer integrated into the dental treatment unit, the system can acquire spectral data and light spot images in real time. The data processing unit calculates the spectral peak wavelength and spectral width using Fourier transform or Gaussian fitting, and quantifies the uniformity of energy distribution within the light spot using statistical methods such as standard deviation or coefficient of variation. When the degradation of any light quality indicator continues to exceed its threshold and meets the duration requirement, the system records it as a curing lamp light quality degradation event point.

[0092] After identifying the two event points, the system compares their timestamps. For example, if the water system state change event occurs at T1 and the light curing lamp quality degradation event occurs at T2, the time interval ΔT = T2 - T1 is calculated. Then, it determines whether ΔT falls within the time delay adjustment range set by the adjustment factor constructed from the heat transfer efficiency index and ambient temperature. For example, if the range is [5 minutes, 15 minutes] and ΔT is 10 minutes, then a correlation between the two is confirmed. This identification mechanism provides an accurate basis for subsequent performance degradation source determination, effectively enhancing the understanding and capture of thermally coupled causal chains.

[0093] In another embodiment of this application, S4000 is further proposed: the step of determining the source of performance degradation based on the chronological order and time interval further includes:

[0094] S4200: Obtain the rate of decrease in uniformity of spot energy distribution, the rate of shift in spectral peak wavelength, and the region of uneven spot energy distribution corresponding to the degradation of the light curing lamp's light quality.

[0095] S4300: Determine the degradation event mode of light curing lamp quality based on the rate of decrease in uniformity of light spot energy distribution, the rate of shift in wavelength of spectral peak, and the region of uneven light spot energy distribution;

[0096] S4400: Based on the degradation event pattern of curing lamp light quality, and combined with whether the time sequence and time interval continuously and stably fall within the time delay adjustment range, it determines whether the degradation source of performance degradation is caused by the degradation of curing lamp light quality.

[0097] The rate of decrease in the uniformity of light spot energy distribution refers to how quickly the spatial uniformity of the energy distribution of the light spot output by the curing lamp changes over time. Specifically, it can be obtained by calculating the rate of change of the standard deviation or coefficient of variation of the light energy density in different regions within the light spot over time. Its purpose is to quantify the decay trend of the curing lamp's illumination uniformity. The rate of change in the spectral peak wavelength refers to how quickly the position of the wavelength with the strongest energy in the output spectrum of the curing lamp changes over time. Specifically, it can be obtained by continuously monitoring the spectral data collected by a spectrometer and calculating the rate of change of its peak wavelength over time. Its purpose is to quantify the decay trend of the curing lamp's luminous color stability. Uneven light spot energy distribution areas refer to the areas where the curing lamp output... The specific spatial range in the light spot where the light energy density is significantly lower or higher than the average level can be obtained by analyzing the energy distribution map of the light spot using image processing technology to identify the regions and boundaries of energy anomalies. The purpose is to locate the specific location of the curing lamp illumination defect. The curing lamp light quality degradation event pattern refers to a specific combination or pattern that comprehensively reflects the characteristics of curing lamp light quality degradation. Specifically, it can be obtained by comprehensively analyzing and classifying multiple parameters such as the rate of decrease in the uniformity of the light spot energy distribution, the rate of shift in the wavelength of the spectral peak, and the region of uneven energy distribution in the light spot to form a representative degradation type identifier. The purpose is to provide a refined basis for subsequent determination of the degradation source.

[0098] The solution proposed in this application introduces a more detailed quantitative description of the quality degradation of light curing lamps and integrates it with time information to make an accurate distinction of the source of performance degradation.

[0099] In some preferred embodiments, this application is implemented as follows: In preferred embodiments, this application accurately acquires multiple key parameters of light quality degradation in curing lamps and combines them with preset degradation mode rules to identify light quality degradation event patterns and determine the source of degradation. First, to acquire the rate of decrease in the uniformity of light spot energy distribution, the rate of wavelength shift of spectral peaks, and the region of uneven light spot energy distribution, the system uses a high-precision spectrometer and a light spot analyzer for data acquisition. The light spot analyzer periodically acquires two-dimensional energy distribution images of the light spot, and the system calculates the rate of decrease in the uniformity of light spot energy distribution by analyzing the change of the standard deviation of light energy density in the image over time. The spectrometer monitors the spectral output in real time, and obtains the rate of wavelength shift of spectral peaks by identifying spectral peaks and calculating their rate of change over time.

[0100] For regions with uneven light spot energy distribution, the system identifies areas where the light energy density significantly deviates from the average value through threshold segmentation or cluster analysis, extracting their geometric features such as coordinates, size, and shape. Subsequently, the system matches these three key parameters with a pre-defined rule base for curing lamp light quality degradation patterns. This rule base contains different degradation types and their corresponding parameter ranges and feature descriptions. For example, if the rate of decrease in light spot energy distribution uniformity exceeds a set threshold, and a significant energy depression appears at the center of the light spot, the system may identify it as "local aging of the LED chip"; if the spectral peak wavelength continuously shifts towards longer wavelengths, the system may determine it as "LED overheating." Finally, based on the identified light quality degradation event patterns, and combined with the temporal sequence and time interval between the water system state change event points and the light quality degradation event points obtained through time-series correlation analysis, the system determines the source of performance degradation. For example, if the curing lamp light quality degradation event point occurs earlier than or simultaneously with the water system state change event point, and is identified as the "LED chip aging" pattern, the system determines that the degradation source is a problem with the curing lamp itself. Conversely, if the water system state change event occurs significantly earlier than the light quality degradation event, and a clear causal temporal relationship exists between the two, then the degradation of the light curing lamp's performance is determined to be caused by the water system state change. This solution effectively enhances the accuracy of identifying the causes of performance degradation in dental integrated treatment machines and improves the reliability and precision of the diagnostic system by constructing a complete analysis chain from multi-parameter degradation feature identification and event pattern matching to temporal logic judgment.

[0101] In another embodiment of this application, S4300 is further proposed to include:

[0102] S4310: Calculate the degree of anomaly for the rate of decrease in the uniformity of the light spot energy distribution and the rate of shift in the wavelength of the spectral peak, respectively. The degree of anomaly calculation is based on the corresponding preset threshold and the corresponding deviation amplitude.

[0103] S4320: Compare the degree of abnormality in the rate of decrease in the uniformity of the light spot energy distribution with the degree of abnormality in the rate of wavelength shift of the spectral peak, and identify the one with the greater degree of abnormality as the dominant indicator of light degradation.

[0104] S4330: Analyze the geometric characteristics of regions with uneven energy distribution in the light spot. Geometric characteristics include shape, size, and location.

[0105] S4340: Determine the event mode of light quality degradation of curing lamps based on the dominant light degradation index and geometric characteristics.

[0106] The anomaly calculation refers to quantifying the severity of deviations from the normal range in the rate of decrease in the uniformity of the spot energy distribution or the rate of shift in the spectral peak wavelength. This can be achieved using statistical methods such as standard deviation, Z-scores, or percentiles, or rule-based methods such as piecewise linear or nonlinear functions. The preset threshold is the critical value used to define the normal and abnormal states in the anomaly calculation. It can be set based on historical data analysis, expert experience, or industry standards, aiming to provide a benchmark for quantifying the anomaly. The deviation magnitude refers to the degree of difference between the current measurement and the preset threshold. It can be expressed as an absolute difference, relative percentage, or normalized difference, reflecting the degree to which the current light quality parameters deviate from the normal state. The dominant light degradation index is the parameter that has a more significant impact on the light quality degradation of the curing lamp, between the rate of decrease in the uniformity of the spot energy distribution and the rate of shift in the spectral peak wavelength. It can be determined based on the comparison results of the anomalies, aiming to identify the main factors causing light quality degradation and simplify subsequent pattern judgment. The geometric characteristics of uneven energy distribution areas within a light spot refer to the physical properties of these areas, specifically their shape, size, and location. These characteristics can be acquired using image processing techniques such as edge detection, region segmentation, or feature extraction algorithms. The aim is to provide spatial information about the degradation of light spot energy distribution, thus providing a more comprehensive description of the degradation pattern. The light curing lamp quality degradation event pattern refers to the specific type or manifestation of light curing lamp quality degradation. It can be defined based on a combination of dominant light degradation indicators and geometric characteristics. The purpose is to classify light quality degradation, providing a basis for subsequent fault diagnosis and repair.

[0107] The proposed solution achieves accurate identification of light curing lamp quality degradation event modes by quantitatively analyzing the rate of decrease in the uniformity of light spot energy distribution and the rate of shift in the wavelength of spectral peaks, and by combining this with the spatial characteristics of the uneven region of light spot energy distribution.

[0108] In some preferred embodiments, this application is implemented as follows: When it is necessary to determine the degradation event mode of light curing lamp light quality, the system first obtains the current spot energy distribution uniformity decrease rate and spectral peak wavelength shift rate. For example, a preset threshold for the spot energy distribution uniformity decrease rate can be set to 80% of its initial value, and the deviation can be calculated as the percentage difference between the current value and the threshold. Similarly, the preset threshold for the spectral peak wavelength shift rate can be set to ±5 nanometers, and the deviation can be calculated as the absolute difference between the current shift and the threshold. The system calculates the degree of abnormality of these two parameters respectively. For example, if the calculated degree of abnormality for the spot energy distribution uniformity decrease rate is 0.7, and the calculated degree of abnormality for the spectral peak wavelength shift rate is 0.3, the system identifies the spot energy distribution uniformity decrease rate as the dominant indicator of light degradation. At the same time, the system analyzes the uneven spot energy distribution area through image processing algorithms. For example, it identifies an elliptical uneven area at the edge of the spot, which occupies 15% of the total spot area and is located in the lower right corner of the spot. Ultimately, based on the dominant light degradation index (abnormal rate of decrease in the uniformity of light spot energy distribution) and geometric features (an elliptical non-uniform area in the lower right corner), the system determines the current light curing lamp quality degradation event mode as "local energy attenuation degradation at the light spot edge". Identifying this mode can help technicians quickly locate problems, such as checking for damage to the fiber optic ends or aging in specific areas of the LED array.

[0109] In another embodiment of this application, S4340 further includes:

[0110] S4341: Obtain the preset curing lamp light quality degradation mode rules. The curing lamp light quality degradation mode rules include the range of light degradation dominant indicators and the range of geometric features corresponding to multiple curing lamp light quality degradation event modes.

[0111] S4342: Match the dominant light degradation index and geometric features with the light quality degradation mode rules of the curing lamp to obtain the matching results;

[0112] S4343: When multiple matching results exist, perform controlled disturbances, including adjusting the instantaneous temperature of the water circuit or adjusting the output power of the light curing lamp;

[0113] S4344: During controlled disturbance execution, collect dynamic changes in the quality data of the curing lamp light;

[0114] S4345: Extract response features of light quality degradation event patterns of curing lamps based on dynamic changes in light quality data;

[0115] S4346: Match the response features with the response curves of multiple matching results containing curing lamp light quality degradation event modes to determine the curing lamp light quality degradation event mode with the highest matching degree.

[0116] The curing lamp light quality degradation pattern rule refers to a pre-established dataset describing the characteristics of different curing lamp light quality degradation event patterns. This dataset can be a series of records stored in a database or lookup table. Each record contains an identifier for a specific curing lamp light quality degradation event pattern, along with the numerical range of the dominant light degradation indices (e.g., the rate of decrease in spot energy distribution uniformity or the rate of spectral peak wavelength shift) and the descriptive range of geometric features (e.g., the shape, size, and location of uneven spot energy distribution areas) associated with that pattern. The matching result refers to the set of one or more potential curing lamp light quality degradation event patterns that conform to the rule definition, obtained by comparing the currently acquired dominant light degradation indices and geometric features with the curing lamp light quality degradation pattern rule. This set can be a list containing one or more pattern identifiers. Controlled disturbance refers to the deliberate and reversible adjustment of specific system parameters under normal operating conditions of a dental treatment machine to induce an observable dynamic response in the curing lamp's light quality data. This can be achieved by adjusting the instantaneous temperature of the water circuit or the output power of the curing lamp through the control system. The purpose is to observe the system's response to known external stimuli and obtain unique dynamic response information under different degradation modes, thereby helping to distinguish similar degradation modes. The dynamic change in the curing lamp's light quality data refers to the continuous, trend-based, or instantaneous numerical changes in various quality indicators of the curing lamp's output light (such as spectral peak wavelength, spectral width, and energy distribution map within the light spot) over time during the controlled disturbance process. This can be a curve showing the change in light intensity over time, a trend in the change of spectral peak wavelength over time, or differences in the light spot energy distribution map at different time points. The purpose is to capture the real-time performance response of the curing lamp under specific stimuli. The response characteristics refer to quantitative indicators extracted from the dynamic changes in curing lamp light quality data, which characterize the response of a specific curing lamp light quality degradation event pattern to a controlled disturbance. These can include response amplitude, transient response time, and settling time. The response curve refers to a pre-established set of dynamic trends or characteristics describing the light quality data of different curing lamp light quality degradation event patterns under a specific controlled disturbance. It can be obtained through experiments, simulations, or historical data analysis, representing typical curves or sets of characteristic parameters that show the change of light quality data over time under a specific degradation pattern. Its purpose is to provide a standardized reference model for matching response characteristics, thereby achieving accurate identification of curing lamp light quality degradation event patterns.

[0117] This solution introduces a dynamic response analysis mechanism to further and more accurately identify the initially determined light curing lamp quality degradation event patterns.

[0118] In some preferred embodiments, this solution is implemented as follows: First, the system acquires preset light curing lamp light quality degradation mode rules. These rules can be stored in a structured database, for example, a database table named "DegradationModeRules," which contains fields such as "ModeID," "DominantIndicatorRange," and "GeometricFeatureRange." For example, for the "LED chip aging" mode, the range of its dominant light degradation index (such as the spectral peak wavelength shift rate) can be set to "0.5nm / 100h to 1.0nm / 100h," while the range of geometric features (such as uneven energy distribution areas) can be described as "the uniformity of the central region decreases, while the edge region remains relatively stable." For the "heat dissipation system efficiency degradation" mode, the range of its dominant light degradation index (such as the rate of decrease in the uniformity of the light distribution area) can be set to "0.8% / 100h to 1.5% / 100h," while the geometric features can be described as "significant energy decrease occurs in the edge region of the light spot." Next, the currently detected dominant light degradation index and geometric features are matched with these preset rules. For example, if the detected spectral peak wavelength shift rate is 0.6 nm / 100 h, and the uneven energy distribution area is mainly in the center, the system will query the database and find that it may match both "LED chip aging" and "driver circuit abnormality" modes simultaneously, thus obtaining multiple matching results. When multiple matching results exist, the system will execute controlled perturbations. Specifically, if the matching result includes the possibility of "decreased heat dissipation system efficiency," the system can adjust the instantaneous water circuit temperature. For example, by controlling the water circuit heater, the instantaneous water circuit temperature can be briefly increased from the normal operating temperature (e.g., 37°C) to 40°C and maintained for 30 seconds. If the matching result includes the possibility of "LED chip aging," the system can adjust the output power of the curing lamp. For example, the output power of the curing lamp can be increased from the conventional 1200 mW / cm². 2 Adjust to 800mW / cm 2The system maintains this state for 15 seconds, then resumes normal operation. During the controlled disturbance, the system continuously collects dynamic changes in the curing lamp's light quality data. For example, when the instantaneous water temperature is adjusted, the system collects the peak wavelength of the curing lamp's spectrum and the energy distribution map within the light spot at a high frequency of 100 times per second, recording its real-time response to temperature changes. When the output power of the curing lamp is adjusted, the system similarly collects light intensity and spectral width data at a high frequency. Based on these dynamic changes in light quality data, the system extracts the response characteristics of the curing lamp's light quality degradation event patterns. For example, for light intensity data, the system can determine its baseline state before the controlled disturbance begins (e.g., 1200 mW / cm²). 2 The system identifies the point in time when the light intensity deviates from the baseline and continues to exceed a preset deviation threshold (e.g., 5%) as the transient response start point, and the point in time when the light intensity reaches and remains within a preset stable range (e.g., ±2%) after the transient response start point as the stable end point. Then, based on these time points and values, the system calculates the response amplitude (e.g., the maximum change in light intensity), transient response time (the time from the start of the disturbance to the transient response start point), and settling time (the time from the transient response start point to the stable end point). These calculated response amplitudes, transient response times, and settling times serve as the response characteristics of the light curing lamp's light quality degradation event mode. Finally, the system matches these extracted response characteristics with preset response curves included in multiple matching results. These response curves can be pre-established using extensive experimental data. For example, for the "LED chip aging" mode, the light intensity response curve under power reduction disturbances may exhibit a slow decrease and a long recovery time; while for the "drive circuit abnormality" mode, the light intensity response curve under power reduction disturbances may exhibit a rapid decrease and a short recovery time. The system will calculate the similarity between the current response features and each preset response curve (e.g., using Euclidean distance or correlation coefficient), and determine the light curing lamp quality degradation event pattern with the highest matching degree as the final diagnostic result.

[0119] In another embodiment of this application, S4345 further includes:

[0120] S43451: Determine the baseline state of the quality data of the curing lamp light before the start of controlled disturbance;

[0121] S43452: Identify the time point at which the quality data of the curing lamp light starts to deviate from the baseline state and continues to exceed a preset deviation threshold, and determine the transient response start point;

[0122] S43453: Identify the time point at which the quality data of the light curing lamp reaches and remains within a preset stable range after the transient response start point, and determine the stable end point;

[0123] S43454: Calculate the response amplitude based on the baseline state, the transient response start point, and the steady-state end point;

[0124] S43455: Calculate the transient response time based on the start time of the controlled disturbance and the starting point of the transient response;

[0125] S43456: Calculate the settling time based on the transient response start point and the steady-state end point;

[0126] S43457: Response amplitude, transient response time, and settling time are used as response characteristics of the light curing lamp quality degradation event mode.

[0127] The baseline state refers to the stable state of the curing lamp light quality data when it is not affected by controlled disturbances. Specifically, it can be obtained by continuously monitoring and averaging the light quality data for a period of time before applying the disturbance. The preset deviation threshold is a preset numerical limit used to determine whether the light quality data begins to deviate from the baseline state. Specifically, it can be set through empirical data, statistical analysis, or simulation models. The transient response start point is the time point at which the curing lamp light quality data begins to deviate from the baseline state and continues to exceed the preset deviation threshold. Specifically, it can be identified by real-time monitoring of the difference between the light quality data and the baseline state, combined with a continuous judgment within a time window. The preset stability range refers to the preset numerical range within which the curing lamp light quality data reaches and remains after the transient response. Specifically, it can be determined by statistical analysis of the fluctuation range during normal system operation. The stability end point refers to the preset stability range within which the curing lamp light quality data reaches and remains after the transient response start point. The time points within a certain range can be identified by continuously monitoring whether the light quality data remains within a preset stable range over a period of time; the response amplitude refers to the maximum change in light quality data from the baseline state to the stable endpoint under controlled disturbance, or the difference between the final stable value and the baseline value. Specifically, it can be obtained by calculating the difference between the peak value of the light quality data during the transient response process and the baseline state, or the difference between the value at the stable endpoint and the baseline state; the transient response time refers to the time interval between the start time of the controlled disturbance and the starting point of the transient response. Specifically, it can be calculated by recording the precise time point of the controlled disturbance application and the identification time of the transient response starting point, and its purpose is to measure the response speed of the light quality data to the disturbance; the settling time refers to the time interval between the starting point of the transient response and the stable endpoint, and specifically, it can be calculated by recording the identification time of the transient response starting point and the stable endpoint, and its purpose is to measure the time required for the light quality data to reach a stable state.

[0128] The solution proposed in this application conducts an in-depth analysis of the dynamic changes in the quality data of curing lamps under controlled perturbations, thereby more accurately identifying the degradation event patterns of curing lamp light quality.

[0129] In some preferred embodiments, this application is implemented as follows: Assuming that during the testing process of a dental integrated treatment machine, when static analysis identifies multiple degradation modes in the light-curing lamp, the system will perform a controlled perturbation, such as adjusting the instantaneous water temperature from 25 degrees Celsius to 35 degrees Celsius, and simultaneously collecting data on the uniformity of the light spot energy distribution of the light-curing lamp. Before applying the water temperature perturbation, the system will continuously monitor the uniformity of the light spot energy distribution for 10 seconds and calculate the average value of the data within these 10 seconds, defining it as the baseline state, for example, the uniformity of the light spot energy distribution in the baseline state is 95%. Subsequently, the system continues to monitor the uniformity of the light spot energy distribution. When the data starts to decrease from 95%, and the decrease exceeds a preset deviation threshold (e.g., 2%) within 3 consecutive seconds, i.e., falls below 93%, the system will record this time point and define it as the transient response start point. After the transient response initiation point, the system continues to monitor data. When the uniformity of the light spot energy distribution remains within a preset stable range (e.g., 88% to 90%) for the next 5 seconds, the system records this time point and designates it as the stable termination point. Based on these determined time points and values, the system can calculate the response characteristics. For example, if the baseline state is 95% and the data at the stable termination point is 89%, then the response amplitude can be calculated as 95% minus 89%, which is 6%. If the controlled disturbance starts at time T0 and the transient response initiation point is at T0+2 seconds, then the transient response time is 2 seconds. If the transient response initiation point is at T0+2 seconds and the stable termination point is at T0+10 seconds, then the stabilization time is 8 seconds. Finally, the calculated response amplitude of 6%, transient response time of 2 seconds, and stabilization time of 8 seconds are used as the response characteristics of the light curing lamp quality degradation event mode. These features can be matched with preset degradation mode response curves to accurately identify the current degradation mode of the curing lamp, such as overheating degradation caused by decreased water cooling efficiency, or other reasons.

[0130] In another embodiment of this application, the sub-step of S5000, which outputs a diagnostic report based on the chronological order, time interval, and source of degradation, includes:

[0131] S5100: Matches the light curing lamp light quality degradation event mode with the preset performance degradation mode classification rules to determine the degradation type of the current performance degradation of the dental comprehensive treatment machine;

[0132] S5200: Generates a fault source identifier based on the degradation type and degradation source. The fault source identifier indicates the abnormal performance of the corresponding subsystem of the dental comprehensive treatment machine.

[0133] S5300: Writes the degradation type, degradation source, and fault source identifier into the diagnostic report and outputs the diagnostic report.

[0134] The preset performance degradation mode classification rules refer to a pre-established knowledge base or algorithm set used to identify and classify the performance degradation phenomena of dental comprehensive treatment machines. It can contain a variety of known degradation modes and their corresponding feature descriptions. For example, it can be implemented based on machine learning models or expert system rule bases. The degradation type refers to the specific category to which the current performance degradation of the dental comprehensive treatment machine is classified, such as "insufficient light intensity degradation", "spectral shift degradation" or "decreased heat dissipation efficiency degradation", etc. The fault source identifier refers to a mark or description that clearly indicates which subsystem or component inside the dental comprehensive treatment machine has a performance abnormality. It can be a text string, a code or a pointer to a specific fault point. Its purpose is to provide maintenance personnel with direct fault location information. The diagnostic report refers to a document or data structure containing diagnostic information on the performance degradation of the dental comprehensive treatment machine. It can contain key information such as degradation type, degradation source and fault source identifier. Its purpose is to provide comprehensive guidance for repair and maintenance.

[0135] The solution proposed in this application determines the degradation type of the current performance degradation of the dental integrated treatment machine by matching the light quality degradation event pattern of the curing lamp with the preset performance degradation pattern classification rules. Then, a fault source identifier is generated according to the degradation type and degradation source, and finally, this information is written into the diagnostic report and output.

[0136] In one optional embodiment, this application is implemented as follows: When matching the degradation event patterns of curing lamp light quality with preset performance degradation pattern classification rules, a machine learning model based on decision trees or support vector machines (SVM) can be used. For example, the preset performance degradation pattern classification rule can be a pre-trained decision tree model, whose input is the response features of the curing lamp light quality degradation event patterns (such as response amplitude, transient response time, and settling time), and the output is a clear degradation type. When a new curing lamp light quality degradation event pattern is received, its response features are input into the decision tree model, and the model will classify it into the most suitable degradation type according to its internal judgment logic, such as "LED chip aging degradation" or "heat dissipation system efficiency deterioration degradation".

[0137] When generating fault source identifiers based on degradation type and degradation source, a fault mapping table can be preset. This mapping table maps different combinations of degradation types and degradation sources to specific fault source identifiers. For example, the mapping table can contain entries such as: "Degradation type: LED chip aging degradation + Degradation source: UV curing lamp itself -> Fault source identifier: UV curing lamp LED array performance degradation"; or "Degradation type: Heat dissipation efficiency reduction degradation + Degradation source: Water system -> Fault source identifier: Partial blockage of water cooling pipes". Once the system determines the degradation type and degradation source, it can query this mapping table to directly obtain the corresponding fault source identifier.

[0138] When writing the degradation type, degradation source, and fault source identifier into the diagnostic report and outputting it, the diagnostic report can be a structured electronic document, such as an XML or JSON file, or a printable PDF report. The report can include the diagnostic time, device serial number, testing method version, determined degradation type, identified degradation source, and generated fault source identifier. For example, the report could clearly state: "Diagnostic Result: Performance degradation of the UV curing lamp; Degradation Type: Spectral peak wavelength shift; Degradation Source: The UV curing lamp itself; Fault Source Identifier: Abnormal LED driver circuit of the UV curing lamp causing spectral drift."

[0139] Through the above technical solution, this application can fully utilize the information contained in the light curing lamp quality degradation event mode, and by matching it with the preset performance degradation mode classification rules, it can identify the specific type of current performance degradation of the dental comprehensive treatment machine.

[0140] In another embodiment of this application, a sub-step of S2000 is further proposed: during the execution of the load sequence, synchronously acquiring water system status data and light curing lamp light quality data includes:

[0141] S2100: Sets a unified sampling clock reference and performs synchronous acquisition of water system status data and light curing lamp light quality data based on the sampling clock reference;

[0142] S2200: Acquire the rate of change of instantaneous temperature and the rate of change of water flow rate in the water path respectively, and adjust the dynamic sampling frequency of the water path system status data based on the rate of change of instantaneous temperature and the rate of change of water flow rate in the water path.

[0143] S2300: Sets a fixed high-frequency sampling frequency for the spectral peak wavelength, spectral width, and energy distribution map inside the light spot, and performs data integrity verification within the specified sampling period;

[0144] S2400: Generates a timestamp and records the corresponding dynamic sampling frequency and fixed high-frequency sampling frequency in each sampling period, and constructs a time synchronization index table;

[0145] S2500: Based on the time synchronization index table, it realizes data timeline alignment and dynamic registration during time series correlation analysis.

[0146] The sampling clock reference refers to a unified time reference point or frequency source used to coordinate the time synchronization of different data acquisition channels. It can be implemented using a hardware clock chip, a Network Time Protocol (NTP) server, or a high-precision crystal oscillator. The rate of change refers to the amount of change in instantaneous water temperature or flow rate per unit time. It can be obtained using algorithms such as differential calculation, moving average, or Kalman filtering on continuously acquired data points. The dynamic sampling frequency refers to the sampling frequency adjusted in real time according to the rate of change of the water system status data. It can be implemented using step-wise adjustment based on a preset threshold, PID control algorithms, or adaptive filtering algorithms to increase the sampling frequency when data changes drastically and decrease it when data is stable. Data integrity verification refers to checking the acquired light curing lamp quality data to ensure that the data has not been lost, damaged, or tampered with during transmission and storage. It can be performed using methods such as Cyclic Redundancy Check (CRC), checksum algorithms, or packet sequence number checks. Data timeline alignment and dynamic registration refers to mapping water system status data and light curing lamp quality data collected at different sampling frequencies onto a unified timeline based on their timestamps and sampling frequency information, and performing precise time point matching. This can be achieved using algorithms such as linear interpolation, spline interpolation, nearest neighbor matching, or lookup and mapping based on a time synchronization index table.

[0147] The solution proposed in this application solves the problem of synchronous acquisition caused by the difference in data quality characteristics between the water system and the light curing lamp during the execution of a load sequence in a dental comprehensive treatment machine through a refined data acquisition and time synchronization mechanism, thereby ensuring the accuracy of subsequent time-series correlation analysis.

[0148] In some preferred embodiments, this application is implemented as follows: First, to establish a unified sampling clock reference, a high-precision hardware real-time clock module (such as the DS3231 chip) or synchronization via a Network Time Protocol (NTP) server can be used to ensure that all data acquisition modules in the system share the same time reference. Specifically, a microcontroller such as the STM32 series can be used as the main controller to control two independent analog-to-digital converter (ADC) channels, respectively connected to the water system sensor and the light curing lamp sensor. This microcontroller can be configured with a unified timer interrupt to trigger synchronous sampling of the two ADC channels at a fixed frequency, achieving synchronous acquisition of water system status data and light curing lamp light quality data.

[0149] To obtain the rate of change of instantaneous temperature and flow rate in the water circuit, the system performs a first-order difference on the continuous sampled values, i.e., subtracting the previous value from the current value and dividing by the sampling time interval. Based on the obtained rate of change, the system dynamically adjusts the sampling frequency of the water circuit system state data. For example, when the rate of change is below a first threshold, the sampling frequency is 1Hz; when it is between the first and second thresholds, it is increased to 5Hz; and when it is above the second threshold, it is increased to 10Hz to more effectively capture rapid changes. In contrast, the spectral peak wavelength, spectral width, and energy distribution map within the light spot use a fixed high-frequency sampling frequency, such as 100Hz, to ensure sensitive capture of fluctuations in the quality of the light curing lamp.

[0150] During each sampling period, the system performs integrity checks on the collected data. Specifically, a Cyclic Redundancy Check (CRC) code is appended to the end of each data packet. The receiving end recalculates and compares the CRC value to determine if a transmission error or data loss has occurred. If an anomaly is found, a retransmission mechanism is triggered or the data is marked as invalid. In addition, the system generates microsecond-level timestamps within the sampling period and records the dynamic sampling frequency used by the water system and the fixed high-frequency sampling frequency used by the light curing lamp. This information is encapsulated into a metadata structure and stored as a time synchronization index table. This index table is a set of key-value pairs, where the key is the timestamp and the value is the corresponding data file location and sampling frequency information.

[0151] In subsequent time-series correlation analysis, the analysis module can call the time synchronization index table to read timestamps and sampling frequency information, completing data timeline alignment and dynamic registration. If the sampling frequency of the water system data is lower than that of the curing lamp data, the system can generate virtual data points between the water system sampling points using linear interpolation or spline interpolation algorithms based on the sampling frequency recorded in the index table, aligning its timeline with the high-frequency data of the curing lamp. Through this method, even if the original sampling frequencies of different data sources are different, the system can achieve accurate time matching of multi-source data, providing a foundation for high-precision time-series correlation analysis and significantly improving the accuracy and reliability of the detection results.

[0152] Reference Figure 2 In another embodiment of this application, a dental comprehensive treatment machine detection system is further proposed, the system comprising:

[0153] The load execution module 1 is used to execute a load sequence that simulates continuous clinical operation of a dental comprehensive treatment machine. The load sequence is used to induce performance changes in multiple subsystems within the dental comprehensive treatment machine under working load conditions.

[0154] Synchronous acquisition module 2 is used to synchronously acquire water system status data and curing lamp light quality data during the execution of the load sequence. The water system status data includes the instantaneous temperature and flow rate of the water system, and the curing lamp light quality data includes the peak wavelength, spectral width, and energy distribution map inside the light spot.

[0155] The correlation analysis module 3 is used to perform time-series correlation analysis based on the water system status data and the light curing lamp quality data to identify the temporal sequence and time interval between the changes in the water system status and the degradation of the light curing lamp quality.

[0156] Degradation source determination module 4 is used to determine the source of performance degradation based on the time sequence and time interval;

[0157] Output module 5 is used to output a diagnostic report based on the time sequence, time interval, and source of degradation. The diagnostic report indicates the performance degradation of the dental comprehensive treatment machine and the source of the fault.

[0158] This application provides a dental integrated treatment machine testing system that can automatically and efficiently perform performance testing and fault diagnosis of dental integrated treatment machines. By simulating real clinical operating loads, the system can effectively induce and expose the performance changes of multiple subsystems within the equipment under working load conditions, thereby discovering hidden and progressive degradation problems that are difficult to identify using traditional static testing.

[0159] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for testing a dental comprehensive treatment machine, characterized in that, The method includes: A load sequence is executed to simulate continuous clinical operation of a dental comprehensive treatment machine, the load sequence being used to induce performance changes in multiple subsystems within the dental comprehensive treatment machine under workload conditions; During the execution of the load sequence, water system status data and curing lamp light quality data are collected simultaneously. The water system status data includes the instantaneous temperature and flow rate of the water system, and the curing lamp light quality data includes the peak wavelength, spectral width, and energy distribution map inside the light spot. Based on the water system status data and the curing lamp light quality data, a time-series correlation analysis is performed to identify the temporal sequence and time interval between changes in the water system status and degradation of the curing lamp light quality. Based on the chronological order and time interval, determine the source of performance degradation; A diagnostic report is output based on the chronological order and time interval, as well as the source of degradation. The diagnostic report indicates the performance degradation of the dental comprehensive treatment machine and the source of the malfunction. The step of performing time-series correlation analysis based on the water system status data and the light curing lamp quality data to identify the temporal sequence and time interval between changes in the water system status and degradation of the light curing lamp quality includes: The rate of change of the instantaneous temperature of the water channel and the rate of degradation of the quality of the light curing lamp are obtained. Based on the rate of change of the instantaneous temperature of the water channel and the rate of degradation of the quality of the light curing lamp, the heat transfer efficiency index is calculated. Acquire environmental parameters that characterize the external environment state, including ambient temperature; Based on the heat transfer efficiency index and the ambient temperature, an adjustment factor is constructed to set the time delay adjustment interval for time series correlation analysis. Within the time delay adjustment interval, the temporal sequence and time interval between the changes in the state of the water system and the degradation of the quality of the light curing lamp are identified; The method of determining the source of performance degradation based on the chronological order and time interval includes: Based on whether the time sequence and time interval consistently fall within the time delay adjustment range, it is determined whether the performance degradation is caused by changes in the state of the water system.

2. The method for testing a dental comprehensive treatment machine according to claim 1, characterized in that, The step of identifying the temporal sequence and time interval between the changes in the water system state and the degradation of the light curing lamp quality within the time delay adjustment interval includes: Identify the state changes of the water system, determine whether the instantaneous temperature or flow rate of the water system continuously exceeds a preset change threshold, and whether the duration exceeds a preset duration threshold, and determine the event point of the state change of the water system. The degradation of the light curing lamp light quality is identified by determining whether the spectral peak wavelength has shifted, whether the spectral width has broadened, or whether the uniformity of energy distribution inside the light spot has decreased. The time of change of the light curing lamp light quality degradation is also determined to exceed the corresponding threshold duration, thus identifying the light curing lamp light quality degradation event point. Based on the event points of the water system state change and the event points of the light curing lamp quality degradation, the temporal sequence and time interval between the water system state change and the light curing lamp quality degradation are identified within the time delay adjustment interval.

3. The method for testing a dental integrated treatment machine according to claim 1, characterized in that, The method of determining the source of performance degradation based on the chronological order and time interval also includes: The rate of decrease in uniformity of spot energy distribution, the rate of shift in spectral peak wavelength, and the region of uneven spot energy distribution corresponding to the degradation of the light curing lamp's light quality are obtained. Based on the rate of decrease in the uniformity of the spot energy distribution, the rate of shift in the wavelength of the spectral peak, and the region of uneven spot energy distribution, the degradation event mode of the curing lamp light quality is determined. Based on the aforementioned light curing lamp light quality degradation event pattern, and considering whether the time sequence and time interval consistently fall within the time delay adjustment range, it is determined whether the performance degradation originates from the light curing lamp light quality degradation.

4. The method for testing a dental integrated treatment machine according to claim 3, characterized in that, The step of determining the degradation event mode of the light curing lamp light quality based on the decrease rate of the uniformity of the light spot energy distribution, the shift rate of the spectral peak wavelength, and the region of uneven light spot energy distribution includes: The anomaly degree is calculated for the rate of decrease in the uniformity of the light spot energy distribution and the rate of shift in the wavelength of the spectral peak, respectively, and the anomaly degree calculation is based on the corresponding preset threshold and the corresponding deviation amplitude. By comparing the degree of abnormality in the rate of decrease in the uniformity of the light spot energy distribution with the degree of abnormality in the rate of shift in the wavelength of the spectral peak, the one with the greater degree of abnormality is identified as the dominant indicator of light degradation. Analyze the geometric features of the uneven energy distribution region of the light spot, including its shape, size, and location; Based on the dominant light degradation index and the geometric features, the event mode of light quality degradation of the curing lamp is determined.

5. The method for testing a dental comprehensive treatment machine according to claim 4, characterized in that, The step of determining the degradation event mode of the light curing lamp based on the dominant light degradation index and the geometric features further includes: Obtain preset light curing lamp light quality degradation mode rules, wherein the light curing lamp light quality degradation mode rules include the range of the dominant light degradation index and the range of the geometric features corresponding to multiple light curing lamp light quality degradation event modes; The dominant light degradation index, the geometric features, and the light quality degradation mode rules of the curing lamp are matched to obtain the matching results; When multiple matching results exist, a controlled disturbance is performed, which includes adjusting the instantaneous temperature of the water circuit or adjusting the output power of the light curing lamp; During the controlled disturbance execution process, the dynamic changes in the light curing lamp light quality data are collected; Based on the dynamic changes in the light quality data, the response features of the light quality degradation event mode of the curing lamp are extracted; The response features are matched with the response curves of the curing lamp light quality degradation event modes contained in the multiple matching results, and the matching result with the highest matching degree is determined as the final curing lamp light quality degradation event mode.

6. The method for testing a dental integrated treatment machine according to claim 5, characterized in that, The step of extracting response features of the light quality degradation event pattern of the curing lamp based on the dynamic changes of the light quality data includes: The baseline state of the light curing lamp quality data before the controlled disturbance begins is determined; The transient response start point is determined by identifying the time point at which the quality data of the curing lamp begins to deviate from the baseline state and continues to exceed a preset deviation threshold. Identify the time point at which the quality data of the light curing lamp reaches and remains within a preset stable range after the transient response start point, and determine the stable end point; The response amplitude is calculated based on the baseline state, the transient response start point, and the stable end point. The transient response time is calculated based on the start time of the controlled disturbance and the starting point of the transient response. Calculate the settling time based on the transient response start point and the stable end point; The response amplitude, transient response time, and settling time are used as response characteristics of the light curing lamp quality degradation event mode.

7. The method for testing a dental integrated treatment machine according to claim 6, characterized in that, The process of outputting a diagnostic report based on the chronological order, time interval, and source of degradation includes: The degradation event pattern of the light curing lamp quality is matched with the preset performance degradation pattern classification rules to determine the degradation type of the current performance degradation of the dental comprehensive treatment machine; Based on the degradation type and the degradation source, a fault source identifier is generated, which indicates the abnormal performance of the subsystem corresponding to the dental comprehensive treatment machine; The degradation type, degradation source, and fault source identifier are written into the diagnostic report, and the diagnostic report is output.

8. The method for testing a dental comprehensive treatment machine according to claim 1, characterized in that, During the execution of the load sequence, the water system status data and the light curing lamp quality data are collected synchronously, including: A unified sampling clock reference is set, and based on the sampling clock reference, the status data of the water system and the quality data of the light curing lamp are synchronously collected; The rate of change of the instantaneous temperature of the water path and the rate of change of the water flow rate are obtained respectively, and the dynamic sampling frequency of the water path system status data is adjusted based on the rate of change of the instantaneous temperature of the water path and the rate of change of the water flow rate. A fixed high-frequency sampling frequency is set for the spectral peak wavelength, the spectral width, and the energy distribution map inside the light spot, and data integrity verification is performed within a specified sampling period; In each sampling period, a timestamp is generated and the corresponding dynamic sampling frequency and fixed high-frequency sampling frequency are recorded to construct a time synchronization index table. Based on the time synchronization index table, data timeline alignment and dynamic registration are achieved during the time-series correlation analysis process.

9. A dental integrated treatment machine detection system, characterized in that, The system includes: The load execution module is used to execute a load sequence that simulates continuous clinical operation of a dental comprehensive treatment machine. The load sequence is used to induce performance changes in multiple subsystems within the dental comprehensive treatment machine under working load conditions. The synchronous acquisition module is used to synchronously acquire water system status data and light curing lamp quality data during the execution of the load sequence. The water system status data includes the instantaneous temperature and flow rate of the water system, and the light curing lamp quality data includes the peak wavelength, spectral width, and energy distribution map inside the light spot. The correlation analysis module is used to perform time-series correlation analysis based on the water system status data and the light curing lamp quality data to identify the temporal sequence and time interval between the water system status changes and the light curing lamp quality degradation. The degradation source determination module is used to determine the source of performance degradation based on the time sequence and time interval. The output module is used to output a diagnostic report based on the time sequence and time interval and the source of degradation. The diagnostic report indicates the performance degradation of the dental comprehensive treatment machine and the source of the fault. The correlation analysis module is also used for: The rate of change of the instantaneous temperature of the water channel and the rate of degradation of the quality of the light curing lamp are obtained. Based on the rate of change of the instantaneous temperature of the water channel and the rate of degradation of the quality of the light curing lamp, the heat transfer efficiency index is calculated. Acquire environmental parameters that characterize the external environment state, including ambient temperature; Based on the heat transfer efficiency index and the ambient temperature, an adjustment factor is constructed to set the time delay adjustment interval for time series correlation analysis. Within the time delay adjustment interval, the temporal sequence and time interval between the changes in the state of the water system and the degradation of the quality of the light curing lamp are identified; The method of determining the source of performance degradation based on the chronological order and time interval includes: Based on whether the time sequence and time interval consistently fall within the time delay adjustment range, it is determined whether the performance degradation is caused by changes in the state of the water system.

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