Health medical big data supported evaluation method and system for oral cavity prevention intervention of children

Abstract

This invention discloses a method and system for assessing pediatric oral health prevention and intervention supported by big data in health and medical fields. It relates to the fields of medical informatics and health data processing technology, and includes the following steps: collecting data upload time series and anomaly removal trigger records generated during high-concurrency phases; aligning these data according to a unified time scale; constructing a concurrency rhythm graph; and calculating the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. This invention identifies risk distortion segments by constructing a concurrency rhythm graph, anomaly trigger frequency curve, and sample quantity change curve using a unified time scale, revealing the temporal impact of anomaly removal on risk assessment. Furthermore, by adjusting the time entry point to implement phased replay and gradient recovery of the anomaly removal intensity, it achieves data processing rhythm rearrangement, ensuring the temporal continuity and assessment accuracy of risk statistics results.

Description

Technical Field

[0001] This invention relates to the field of medical information technology and health data processing technology, specifically to a method and system for assessing and intervening in children's oral health supported by big data in health and medical care. Background Technology

[0002] Pediatric oral health prevention and intervention assessment supported by big data in health and medicine refers to a technical method that uses multi-source health and medical data generated by children at different time stages as a basis. Through systematic data integration, analysis, and correlation processing, it quantifies and dynamically determines the effectiveness of oral health prevention measures implemented for children. Specifically, this concept uses children's oral examination records, caries occurrence, oral hygiene behavior data, dietary habit information, previous treatment records, and regional public health data as data sources to establish continuous individual or group health records. It analyzes the differences in changes before and after intervention within a unified time dimension, thereby determining the impact of preventive measures such as fluoride varnish, caries education, and fissure sealing on caries incidence, oral hygiene index, and recurrence risk. Its core lies in revealing the evolutionary patterns of oral disease risk through the long-term accumulation and cross-comparison of large-scale data, and providing objective data basis for subsequent intervention strategy optimization and public health decision-making, rather than relying solely on single examination results or experience-based judgments.

[0003] The existing technology has the following shortcomings: In existing technologies, during high-concurrency phases of centralized uploading of health and medical big data, to ensure system efficiency, the filtering intensity of abnormal data is typically automatically increased and the scope of anomaly removal is expanded. During this dynamic adjustment process, rules originally designed to identify duplicate or formatted records are easily over-triggered, leading to the misclassification and direct deletion of some genuine case data as abnormal. Since the mistakenly deleted data often appears in the same time period, the risk statistics sample size drops suddenly, and the risk assessment map may exhibit a falsely stable or even declining trend. This causes the system to fail to reflect the actual growth trend of oral diseases in a timely manner, thereby masking the true risk of a rapid spread of group oral diseases in a short period.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for assessing and intervening in children's oral health based on big data in health and medical care, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing and intervening in children's oral health supported by big data in health and medical care, comprising the following steps: Collect the data upload time series and anomaly removal trigger records generated during the high-concurrency phase, align them according to a unified time scale, construct a concurrency rhythm map, and calculate the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. Synchronous statistics are performed on the data upload time series around the anomaly trigger frequency curve to calculate the number of valid samples per unit time, forming a sample number change curve, and anomaly trigger expansion markers are generated at the time positions corresponding to the rising anomaly trigger frequency segment. By using anomaly-triggered expansion markers to compare the sample number change curves in segments, we can identify time segments in which the number of effective samples decreases and the risk curve does not change synchronously within a continuous time segment, extract risk distortion segments, and generate anomaly removal distortion records. Based on the anomaly removal distortion record, trace back the anomaly trigger frequency curve, locate the time range corresponding to the rising anomaly trigger frequency segment, determine the anomaly removal range expansion interval, and form the adjustment time entry point. The abnormal removal process is dynamically rhythmically controlled around the adjustment time entry point. Before the abnormal trigger frequency rises, the data to be removed is replayed. During the abnormal trigger frequency rises, the abnormal removal intensity is reduced and the data is released in segments. During the abnormal trigger frequency recovery segment, the abnormal removal intensity is restored according to the set time gradient, thereby realizing the time rearrangement adjustment of the abnormal removal process.

[0007] Preferably, the steps for forming the abnormal trigger frequency curve are as follows: Collect data upload behavior within a continuous time interval and write it into the data upload time series in the order of occurrence. At the same time, collect the abnormal removal trigger records within the same time interval and mark them with time. The data upload time series and the anomaly removal trigger records are calibrated using the same time base. The time unit is divided into fixed-length time scale units and the timestamps are consolidated to form a time series set under a unified time scale. Data upload behavior within each time scale unit is aggregated and statistically analyzed around a unified time scale to form a data upload distribution sequence. At the same time, anomaly removal trigger records within each time scale unit are statistically analyzed to form an anomaly trigger count sequence. The data upload distribution sequence and the anomaly trigger count sequence are then mapped to a concurrent rhythm diagram. The abnormal trigger frequency sequence is formed by calculating the unit time frequency of the abnormal trigger count sequence based on the concurrent rhythm diagram, and then the abnormal trigger frequency sequence is mapped to the abnormal trigger frequency curve.

[0008] Preferably, the abnormal trigger frequency sequence is embedded into the corresponding time coordinate of the concurrent rhythm graph in chronological order around a unified time scale, and the abnormal trigger frequency values ​​in each time scale unit are arranged continuously to keep the abnormal trigger frequency curve and the data upload time sequence corresponding under the same time scale framework.

[0009] Preferably, the steps for synchronously statistically analyzing the data upload time series based on the anomaly trigger frequency curve are as follows: The data upload time series is mapped according to the time scale unit consistent with the anomaly trigger frequency curve. Within each time scale unit, the records that were not removed by the anomaly are summarized and statistically analyzed to form the number of valid samples per unit time. The effective sample count sequence is formed by arranging the effective sample counts in chronological order around the effective sample counts per unit time, and the effective sample count sequence is mapped to a sample count change curve, keeping the sample count change curve and the anomaly trigger frequency curve on the same time scale frame. The continuously increasing time scale unit around the abnormal trigger frequency curve is divided into segments to form the abnormal trigger frequency rising segment, and the abnormal trigger frequency rising segment is mapped to the time scale unit corresponding to the sample number change curve. Anomaly trigger expansion markers are generated around the rising segment of the anomaly trigger frequency at the corresponding time scale position of the sample number change curve. The anomaly trigger expansion markers cover the time range corresponding to the rising segment of the anomaly trigger frequency.

[0010] Preferably, the abnormal trigger frequency rising segment is determined based on the increasing state of the abnormal trigger frequency within the continuous time scale unit, and the abnormal trigger expansion mark is marked with time coverage according to the start time position and end time position of the abnormal trigger frequency rising segment, and embedded in the time scale system of the sample number change curve.

[0011] Preferably, the steps for segment comparison of the sample quantity change curve using anomaly-triggered expansion markers are as follows: Extract the data corresponding to the sample number change curve around the time scale segment covered by the abnormal trigger expansion marker, form the segment sample number sequence, and identify the segment where the effective sample number decreases within a continuous time segment. Data on the time scale segment corresponding to the risk curve is extracted around the segment where the number of effective samples decreases, forming a segment risk value sequence and identifying the segment where the risk curve has not changed synchronously; The continuous time intervals that simultaneously satisfy the conditions of a decrease in the number of valid samples and no synchronous change in the risk curve are identified, and risk distortion segments are extracted and the start and end time positions are recorded. Information on time segments, segments where the number of effective samples decreases, and segments where the risk curve does not change synchronously is integrated around risk distortion segments to generate anomaly removal distortion records and store them according to a unified time scale.

[0012] Preferably, the anomaly removal distortion record includes the start time position, end time position, change value of effective sample quantity and risk value corresponding to the risk distortion segment. The anomaly removal distortion record and the anomaly trigger expansion mark maintain the same time scale and are arranged in chronological order to form a continuous time segment record.

[0013] Preferably, the steps for backtracking the anomaly trigger frequency curve based on the anomaly removal distortion record are as follows: Read the start and end time positions corresponding to the anomaly removal distortion records according to a unified time scale, and map the corresponding time segments to the anomaly trigger frequency curve to form a segment anomaly trigger frequency sequence. The abnormal trigger frequency sequence is used to identify time segments where the abnormal trigger frequency increases continuously to form abnormal trigger frequency rising segments, and the time range of abnormal trigger frequency rising segments is recorded. The time intervals corresponding to the rising frequency of anomalies and the distorted records of anomaly removal are overlaid to determine the time scale unit covered by the rising frequency of anomalies as the expanded range of anomaly removal. The starting and ending time positions of the intervals around the anomaly removal range are extracted to form adjustment time entry points, and a correspondence is established between the adjustment time entry points and the anomaly removal distortion records.

[0014] Preferably, the steps for dynamically controlling the rhythm of the anomaly removal process around the adjustment time entry are as follows: Extract data records that have been removed from the abnormality before the abnormality trigger frequency rises in the time range corresponding to the adjustment time entry, and re-insert them into the data processing flow in the original time order to complete the playback process; The intensity of the anomaly removal process is adjusted around the time scale unit within the rising range of anomaly trigger frequency. The anomaly removal intensity is adjusted to be lower than the original level, and the data that has been removed from the anomalies is released in segments. The anomaly removal intensity is gradually restored according to a set time gradient around the anomaly trigger frequency recovery section, so that the anomaly removal intensity is consistent with the time range of the anomaly trigger frequency recovery section; The processing results before, during, and after the abnormal trigger frequency rises are integrated using a unified time scale to achieve time rearrangement and adjustment of the abnormal removal process.

[0015] The pediatric oral health prevention and intervention assessment system supported by big data in health and medical care includes a concurrent rhythm construction module, a sample fluctuation analysis module, a distortion identification module, an abnormal interval location module, and a rhythm control module. The concurrency rhythm construction module collects the data upload time series and anomaly removal trigger records formed during the high concurrency phase, aligns them according to a unified time scale, constructs a concurrency rhythm graph, and calculates the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. The sample fluctuation analysis module performs synchronous statistics on the data upload time series around the anomaly trigger frequency curve, calculates the number of valid samples per unit time, forms a sample number change curve, and generates anomaly trigger expansion markers at the time positions corresponding to the rising anomaly trigger frequency. The distortion identification module uses anomaly-triggered expansion markers to compare the sample number change curve in segments, identify time segments in which the number of effective samples decreases and the risk curve does not change synchronously, extract risk distortion segments, and generate anomaly removal distortion records. The abnormal interval positioning module traces back the abnormal trigger frequency curve based on the abnormal removal distortion record, locates the time range corresponding to the rising section of the abnormal trigger frequency, determines the expanded interval of the abnormal removal range, and forms the adjustment time entry point. The rhythm control module dynamically controls the rhythm of the anomaly removal process around the adjustment time entry point. Before the anomaly trigger frequency rises, the removed data is replayed. During the anomaly trigger frequency rises, the anomaly removal intensity is reduced and the data is released in segments. During the anomaly trigger frequency recovery segment, the anomaly removal intensity is restored according to the set time gradient, thus realizing the time rearrangement adjustment of the anomaly removal process.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a concurrent rhythm graph and anomaly trigger frequency curve to synchronously depict the correspondence between data upload behavior and anomaly removal behavior under a unified time scale. This allows the impact of changes in anomaly filtering intensity on sample structure during high-concurrency phases to be fully recorded and traced. Furthermore, by forming a sample quantity change curve and identifying risk distortion segments, the time difference between the decrease in sample quantity and the change in the risk curve is expressed in a structured manner. This avoids time breaks or trend shifts in the risk assessment map caused by dynamic adjustments to anomaly removal intensity, ensuring that the process of risk changes in children's oral diseases maintains a continuous reflection capability in the time dimension.

[0017] This invention adjusts the construction of the time entry point and the time rearrangement of the anomaly removal process to establish an orderly control relationship between the anomaly removal intensity in the pre-, mid-, and post-stages of the anomaly trigger frequency rise range. This enables the phased playback and segmented release of removed data, allowing the data processing rhythm to re-match the actual data generation rhythm. By restoring the anomaly removal intensity according to a set time gradient, the data screening behavior forms a controllable transition state in the time dimension. This ensures operational efficiency while maintaining the temporal integrity of the risk statistical samples, and improves the responsiveness of the pediatric oral health prevention and intervention assessment results to changes in population risk. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the method for evaluating and intervening in children's oral health supported by big data in health and medical care, as described in this invention.

[0020] Figure 2 This is a schematic diagram of the modules of the pediatric oral health prevention and intervention assessment system supported by health and medical big data of the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1 The illustrated health and medical big data-supported assessment method for pediatric oral health prevention and intervention includes the following steps: Collect the data upload time series and anomaly removal trigger records generated during the high-concurrency phase, align them according to a unified time scale, construct a concurrency rhythm map, and calculate the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. To achieve a unified time scale representation and frequency characterization of data upload and anomaly removal behaviors during high-concurrency phases, the specific implementation steps are as follows, focusing on the acquisition, alignment, mapping, and frequency calculation of data upload time series and anomaly removal trigger records: During high-concurrency phases, a data acquisition process is initiated to record all data upload activities occurring within a continuous time interval. Each data upload activity is written into a data upload time series in chronological order. Simultaneously, anomaly removal trigger records triggered within the same time interval are captured and time-stamped, ensuring each record has a corresponding occurrence time. During this process, the data upload time series and anomaly removal trigger records are calibrated using the same time benchmark. Time units are uniformly divided into fixed-length time scale units, and the original timestamps are consolidated. This allows data upload time series and anomaly removal trigger records from different sources to be mapped to a unified time scale unit, forming a set of original time series with a unified time benchmark. This unified time scale processing ensures a synchronous comparison between the data upload time series and anomaly removal trigger records in subsequent processing, providing a temporal dimension foundation for constructing the concurrent rhythm graph.

[0023] After completing the unified time scale calibration, the data upload behavior within each time scale unit is aggregated and statistically analyzed. The number of data uploads and the distribution of data upload batches within each time scale unit are arranged in chronological order to construct a data upload distribution sequence with the time scale as the horizontal axis. At the same time, the number of anomaly removal trigger records within each time scale unit is synchronously counted, and an anomaly trigger count sequence is generated in chronological order. Under the same time scale framework, the data upload distribution sequence and the anomaly trigger count sequence are arranged in parallel to form a one-to-one correspondence between the two on the time axis. Based on this, with the time scale as the main line, the data upload distribution sequence and the anomaly trigger count sequence are jointly mapped into a concurrent rhythm diagram. This allows the concurrent rhythm diagram to reflect the correspondence between the density of data uploads and the anomaly removal trigger situation under the same time scale system, thereby expressing the upload rhythm state in the form of a continuous time spectrum during the high-concurrency phase.

[0024] After the concurrent rhythm graph is formed, the frequency of the anomaly trigger count sequence is calculated per unit time based on the distribution of anomaly removal trigger records in each time scale unit. The number of anomaly removal trigger records in each time scale unit is converted into anomaly trigger frequency values ​​per unit time, and arranged sequentially according to the time scale to form an anomaly trigger frequency sequence. During the formation of the anomaly trigger frequency sequence, the same time scale division method as the concurrent rhythm graph is maintained, so that the anomaly trigger frequency sequence can be directly embedded into the time coordinates corresponding to the concurrent rhythm graph. Subsequently, the anomaly trigger frequency sequence is mapped to the end area of ​​the concurrent rhythm graph as a continuous curve, so that the concurrent rhythm graph further superimposes the anomaly trigger frequency change trend on the basis of the original data upload rhythm expression, realizing an intuitive depiction of the change of anomaly removal trigger frequency over time, thus forming an anomaly trigger frequency curve, and placing the anomaly trigger frequency curve and the data upload time series within the same time scale framework.

[0025] After the anomaly trigger frequency curve is formed, the changes in the anomaly trigger frequency curve within continuous time scale units are sequentially organized. The anomaly trigger frequency values ​​corresponding to each time scale unit are archived according to the time evolution order, maintaining a correspondence with the data upload time series, thus ensuring the anomaly trigger frequency curve has traceability in the time dimension. Based on this, the anomaly trigger frequency curve serves as the input basis for subsequent sample quantity change analysis, enabling subsequent processing to conduct comparative analysis around the time position of the anomaly trigger frequency curve. Through the aforementioned continuous processing process of time scale unification, concurrent rhythm diagram construction, and unit time anomaly trigger frequency calculation, the synchronous expression of data upload behavior and anomaly removal trigger behavior during high-concurrency phases is achieved. This allows the anomaly trigger frequency curve to reflect the entire process of anomaly removal trigger intensity changing over time within a unified time system, providing a stable time reference basis for the construction of subsequent sample quantity change curves and the identification of risk distortion segments.

[0026] Synchronous statistics are performed on the data upload time series around the anomaly trigger frequency curve to calculate the number of valid samples per unit time, forming a sample number change curve, and anomaly trigger expansion markers are generated at the time positions corresponding to the rising anomaly trigger frequency segment. To establish a synchronous statistical relationship between the anomaly trigger frequency curve and the data upload time series, and to generate a sample number change curve and anomaly trigger expansion markers within a unified time scale framework, the specific implementation steps for time scale mapping, effective sample number extraction, curve generation, and time segment identification are as follows: Based on the established anomaly trigger frequency curve and a unified time scale division method, the data upload time series is remapped according to the same time scale units, so that each time scale unit corresponds to a set of data upload records. During the mapping process, the time stamp of each record in the data upload time series is matched one-to-one with the corresponding time scale of the anomaly trigger frequency curve, so that the data upload time series and the anomaly trigger frequency curve are in the same time scale coordinate system. Subsequently, the data upload record set in each time scale unit is filtered, and the records that have not been rejected by the anomaly are identified as valid sample records. The number of valid sample records in the same time scale unit is summarized and counted to obtain the number of valid samples per unit time. Through this unified time scale mapping and valid sample count processing, the number of valid samples per unit time and the anomaly trigger frequency curve maintain a one-to-one correspondence on the time axis, laying the time reference foundation for the construction of the sample number change curve.

[0027] After completing the statistical analysis of the number of valid samples per unit time, the number of valid samples corresponding to each time scale unit is arranged sequentially according to time to form a sequence of valid sample numbers. This sequence of valid sample numbers is then mapped to a curve showing the change in the number of samples. During the mapping process, the time scale division is kept completely consistent with that of the anomaly trigger frequency curve, so that the curve showing the change in the number of samples and the anomaly trigger frequency curve are displayed synchronously within the same time scale framework. At the same time, to enhance the continuous expression within the time dimension, the number of valid samples between adjacent time scale units is continuously connected, so that the curve showing the change in the number of samples forms a continuous trajectory on the time axis. Through this synchronous mapping process, the curve showing the change in the number of samples can reflect the state of the number of valid samples changing over time within the same time scale system as the curve showing the anomaly trigger frequency curve, and provides a visual basis for subsequent comparative analysis of the anomaly trigger frequency change segment.

[0028] After the sample number change curve is formed, the abnormal trigger frequency curve is segmented based on the numerical distribution of the abnormal trigger frequency curve within each time scale unit. The time scale unit where the abnormal trigger frequency continuously increases is divided into abnormal trigger frequency rising segments, and the start and end time positions of each abnormal trigger frequency rising segment are recorded within a unified time scale framework. Subsequently, the time positions of the abnormal trigger frequency rising segments are mapped to the corresponding time scale units of the sample number change curve, so that the abnormal trigger frequency rising segments form identifiable time reference segments in the sample number change curve. Through this time segment mapping process, the abnormal trigger frequency curve and the sample number change curve are established in a time dimension, providing a time positioning basis for generating abnormal trigger expansion markers.

[0029] After mapping the time position of the rising segment of the abnormal trigger frequency to the sample number change curve, an abnormal trigger expansion marker is generated at the time scale position corresponding to the rising segment of the abnormal trigger frequency in the sample number change curve. The abnormal trigger expansion marker is marked in time scale units and is consistent with the start and end time positions of the rising segment of the abnormal trigger frequency, so that the abnormal trigger expansion marker covers the entire time range corresponding to the rising segment of the abnormal trigger frequency on the time axis. During the marker generation process, the abnormal trigger expansion marker is embedded into the time coordinate system of the sample number change curve, so that the sample number change curve not only reflects the trajectory of the change in the number of effective samples in the time dimension, but also reflects the time segment information corresponding to the rising segment of the abnormal trigger frequency. Through the aforementioned unified time scale mapping, effective sample number statistics, sample number change curve generation, and abnormal trigger expansion marker embedding processing, the synchronous statistical and time comparison relationship between the abnormal trigger frequency curve and the sample number change curve is realized, providing a time positioning basis for subsequent risk distortion segment identification based on the abnormal trigger expansion marker.

[0030] By using anomaly-triggered expansion markers to compare the sample number change curves in segments, we can identify time segments in which the number of effective samples decreases and the risk curve does not change synchronously within a continuous time segment, extract risk distortion segments, and generate anomaly removal distortion records. To achieve the correspondence analysis between the changes in sample quantity and the changes in risk curve within the coverage area of ​​the anomaly-triggered expansion marker, and to extract risk distortion segments and generate anomaly removal distortion records within a unified time scale framework, the specific implementation steps are as follows, focusing on time segment mapping, sample quantity change identification, risk curve comparison analysis, and distortion record generation: Given that the sample quantity change curve has embedded anomaly trigger expansion markers, the time scale segment covered by the anomaly trigger expansion markers is used as the starting point for segment analysis. The start and end time positions corresponding to each anomaly trigger expansion marker are read sequentially, and the sample quantity change curve data within that time segment is extracted within a unified time scale framework. During the extraction process, the effective sample quantity values ​​within continuous time scale units are arranged in chronological order to form a segment sample quantity sequence, maintaining complete consistency with the time range of the anomaly trigger expansion markers, so that the segment sample quantity sequence forms a one-to-one correspondence with the anomaly trigger expansion markers in the time dimension. Subsequently, the quantity change trend between continuous time scale units is continuously observed within the segment sample quantity sequence to identify time segments where the effective sample quantity shows a decreasing state within the continuous time scale units. The time location of these decreasing state segments is recorded, thereby initially screening out continuous time segments where the effective sample quantity decreases within the coverage area of ​​the anomaly trigger expansion markers.

[0031] After locating the effective sample number decline segment, the risk curves formed under the same time scale are introduced into the comparative analysis process, ensuring that the risk curves and sample number change curves maintain a completely consistent scale mapping relationship on the time axis. Around the previously located effective sample number decline segment, risk curve segment data corresponding to this time segment are extracted from the risk curves and arranged in chronological order to form a segment risk value sequence. The risk change status between continuous time scale units is observed in the segment risk value sequence to identify the time range within which the risk curve has not undergone synchronous change. No synchronous change is manifested as the risk value remaining stable within continuous time scale units or not showing a change direction consistent with the decline trend of the effective sample number. Through this comparative processing of the sample number change curve and the risk curve under a unified time scale, a cross-comparison relationship is formed between the effective sample number decline segment and the risk curve non-synchronous change segment, thereby determining the continuous time segment that simultaneously satisfies the conditions of effective sample number decline and risk curve non-synchronous change.

[0032] After identifying a continuous time segment that simultaneously satisfies the conditions of a decrease in the number of valid samples and no synchronous change in the risk curve, this continuous time segment is processed for segment extraction. This time segment is then marked as a risk distortion segment within a unified time scale framework. During the marking process, the start and end times of the risk distortion segment, the corresponding change in the number of valid samples, and the corresponding risk value are recorded synchronously, ensuring that the risk distortion segment has a complete representation in both the time and data dimensions. Simultaneously, to maintain the logical connection with the anomaly trigger expansion marker, the risk distortion segment is bound to its corresponding anomaly trigger expansion marker, allowing each risk distortion segment to be traced back to its corresponding anomaly trigger expansion marker segment. This creates a continuous correspondence between the anomaly trigger expansion marker, the sample number change curve, and the risk curve along the time chain.

[0033] After identifying and extracting risk distortion segments, anomaly removal distortion records are generated around these segments. The time segment information of the risk distortion segments, the information on segments where the number of effective samples decreases, and the information on segments where the risk curve does not change synchronously are integrated and written into the anomaly removal distortion record set in chronological order. These records are stored using a unified time scale as the index unit, ensuring that each record corresponds to a specific continuous time segment and maintains a consistent time mapping with the anomaly trigger expansion marker. Through the aforementioned segment comparison, comparative analysis, risk distortion segment extraction, and anomaly removal distortion record generation process, a structured expression of the difference between the sample number change curve and the risk curve within the coverage segment of the anomaly trigger expansion marker is achieved. This makes the anomaly removal distortion records the temporal basis for subsequently locating segments where the anomaly trigger frequency increases and determining the expansion range of the anomaly removal scope. Thus, the identification of risk distortion segments and the generation of anomaly removal distortion records are completed within a unified time scale framework.

[0034] Based on the anomaly removal distortion record, trace back the anomaly trigger frequency curve, locate the time range corresponding to the rising anomaly trigger frequency segment, determine the anomaly removal range expansion interval, and form the adjustment time entry point. To enable further retrospective analysis of the time segments indicated by the distorted records of anomaly removal, and to locate the time range corresponding to the rising anomaly trigger frequency within a unified time scale framework, thereby determining the expansion range of anomaly removal and forming an adjustment time entry point, the specific implementation steps are as follows, focusing on time chain backtracking, segment mapping, range definition, and entry point construction: Based on the established set of anomaly removal and distortion records, the start and end times of each record are read in a unified time scale order. This time segment is then used as the backtracking starting point and mapped to the corresponding time scale unit of the anomaly trigger frequency curve. During the mapping process, the consistency of the time scale between the anomaly removal and distortion records and the anomaly trigger frequency curve is maintained, ensuring that the time segment of the anomaly removal and distortion records can obtain the corresponding frequency value sequence in the anomaly trigger frequency curve. Subsequently, anomaly trigger frequency data that overlaps with the time segment of the anomaly removal and distortion records are extracted from the anomaly trigger frequency curve and arranged in chronological order to form a segment anomaly trigger frequency sequence. This establishes a direct correlation between the anomaly removal and distortion records and the anomaly trigger frequency curve within a unified time frame, providing a time reference basis for locating subsequent anomaly trigger frequency rising segments.

[0035] After completing the time mapping between the anomaly removal distortion record and the anomaly trigger frequency curve, the segment anomaly trigger frequency sequence is observed on a continuous time scale. Time segments in which the anomaly trigger frequency increases within the continuous time scale unit are identified as anomaly trigger frequency rising segments. During the identification process, the start and end times of the anomaly trigger frequency rising segments are recorded and their correspondence with the time segments of the anomaly removal distortion record is maintained. Subsequently, the anomaly trigger frequency rising segments are observed on both sides of the time axis. The time scale units before the continuous increase in the anomaly trigger frequency are continuously compared with the time scale units after the end of the increase to determine the complete time range of the anomaly trigger frequency rising segments. This allows the anomaly trigger frequency rising segments to form a continuous segment expression in the time dimension, thus providing a time boundary basis for determining the expansion range of the anomaly removal scope.

[0036] After determining the time range of the rising anomaly trigger frequency, this time range is overlaid with the time segment covered by the anomaly removal distortion records. The overlapping time scale units are marked to establish a temporal overlap between the rising anomaly trigger frequency segment and the risk distortion segment. Within the overlapping time segment, the frequency values ​​corresponding to the anomaly trigger frequency are matched with the information on the decrease in the number of valid samples reflected in the anomaly removal distortion records, thus establishing a corresponding chain between the data dimension and the risk dimension for the rising anomaly trigger frequency segment. Subsequently, all time scale units covered by the rising anomaly trigger frequency segment are determined as the anomaly removal range expansion interval, so that this interval fully reflects the correlation between the change in anomaly trigger frequency and the risk distortion state on the time axis, thereby completing the definition of the anomaly removal range expansion interval within a unified time scale framework.

[0037] After determining the expanded range of anomaly removal, the starting time of the expanded range is used as the adjustment starting point, and the ending time is used as the adjustment ending point, forming an adjustment time entry within a unified time scale system. The adjustment time entry is expressed in the form of a time segment, and its time scale range is completely consistent with the rising segment of the anomaly trigger frequency, so that the adjustment time entry can serve as the time input position for subsequent dynamic rhythm control processing. At the same time, the adjustment time entry is associated with the corresponding anomaly removal distortion record, making the adjustment time entry traceable in the time dimension and providing a clear time boundary basis for subsequent anomaly removal process adjustments around the adjustment time entry. Through the aforementioned backtracking mapping, rising segment positioning, anomaly removal range expansion determination, and adjustment time entry formation process, a closed-loop time connection between the anomaly removal distortion record and the anomaly trigger frequency curve is achieved, thus laying the foundation for a continuous time chain for subsequent dynamic rhythm control processing.

[0038] The abnormal removal process is dynamically rhythmically controlled around the adjustment time entry point. Before the abnormal trigger frequency rises, the data to be removed is replayed. During the abnormal trigger frequency rises, the abnormal removal intensity is reduced and the data is released in segments. During the abnormal trigger frequency recovery segment, the abnormal removal intensity is restored according to the set time gradient, thereby realizing the time rearrangement adjustment of the abnormal removal process. To perform continuous time rearrangement of the anomaly removal process around the adjustment time entry point, enabling dynamic rhythm control of the anomaly removal behavior within a unified time scale framework, the following steps are carried out sequentially, focusing on the time range division corresponding to the adjustment time entry point, the replay processing of the removed data, the phased adjustment of the anomaly removal intensity, and the recovery process of the anomaly removal intensity according to the time gradient: Based on the established adjustment time entry point, the start and end time positions corresponding to the adjustment time entry point are mapped to a unified time scale system. Data records that were removed due to anomalies are extracted around the time scale unit preceding the rising anomaly trigger frequency segment. During extraction, the data records removed before the rising anomaly trigger frequency segment are organized according to their original time order, ensuring that each removed data record retains its original timestamp and corresponding time scale position. Subsequently, the organized removed data is reinserted into the data processing flow according to time order, allowing the removed data to complete playback processing within the time scale range preceding the rising anomaly trigger frequency segment. During playback processing, the unified time scale remains unchanged, restoring the removed data to its original corresponding time position on the time axis. This completes the time compensation arrangement before the rising anomaly trigger frequency segment, creating a continuous time basis for subsequent anomaly removal intensity adjustment.

[0039] After replaying the data removed before the anomaly trigger frequency rises, the focus shifts to the time scale units within the rising anomaly trigger frequency range. Within a unified time scale framework, the intensity of the anomaly removal process is adjusted. Within this range, the anomaly removal intensity is lowered from its original level, relaxing the trigger conditions and reducing the probability of immediate data removal. Simultaneously, the data removed within this range is released in segments according to time sequence, allowing each segment to enter the data processing flow sequentially within the rising anomaly trigger frequency range. This segmented release process maintains complete consistency with the time range of the rising anomaly trigger frequency range, ensuring the data release rhythm advances synchronously with the time scale. This collaborative processing of reduced anomaly removal intensity and segmented data release is achieved within the rising anomaly trigger frequency range.

[0040] After the abnormal trigger frequency rises, the time is advanced to the abnormal trigger frequency recovery phase. Within this phase, the abnormal removal intensity is gradually restored according to a set time gradient. Within this time gradient framework, the abnormal removal intensity is adjusted in stages according to a predetermined time scale, gradually transitioning from a decreasing state to its original level within continuous time units. The recovery process maintains complete consistency with the time range of the abnormal trigger frequency recovery phase, ensuring the adjustment rhythm of the intensity is synchronized with the time progression of the recovery phase. Simultaneously, newly generated data within the recovery phase is processed according to the restored abnormal removal intensity, allowing the abnormal removal process to smoothly transition from a relaxed state to its original state over time.

[0041] After completing the replay processing before the abnormal trigger frequency rises, the abnormal removal intensity reduction and segmented data release processing within the abnormal trigger frequency rises, and the abnormal removal intensity recovery processing within the abnormal trigger frequency recovery period according to the set time gradient, the processing results of the above three consecutive time periods are integrated in a unified time scale order. This makes the abnormal removal process form a continuous time structure on the overall time axis, with the first segment being replay, the second segment being adjustment, and the third segment being progressive recovery. Through this continuous time rearrangement processing, the abnormal removal process completes dynamic rhythm control within a unified time scale framework, establishing a time correspondence between the changes in abnormal removal intensity and the segments of abnormal trigger frequency change. This achieves time rearrangement adjustment of the abnormal removal process, ensuring the continuity and consistency of data processing behavior in the time dimension, and providing a time structure basis for the subsequent risk assessment curve to recover the true trend of change.

[0042] This invention constructs a concurrent rhythm graph and anomaly trigger frequency curve to synchronously depict the correspondence between data upload behavior and anomaly removal behavior under a unified time scale. This allows the impact of changes in anomaly filtering intensity on sample structure during high-concurrency phases to be fully recorded and traced. Furthermore, by forming a sample quantity change curve and identifying risk distortion segments, the time difference between the decrease in sample quantity and the change in the risk curve is expressed in a structured manner. This avoids time breaks or trend shifts in the risk assessment map caused by dynamic adjustments to anomaly removal intensity, ensuring that the process of risk changes in children's oral diseases maintains a continuous reflection capability in the time dimension.

[0043] This invention adjusts the construction of the time entry point and the time rearrangement of the anomaly removal process to establish an orderly control relationship between the anomaly removal intensity in the pre-, mid-, and post-stages of the anomaly trigger frequency rise range. This enables the phased playback and segmented release of removed data, allowing the data processing rhythm to re-match the actual data generation rhythm. By restoring the anomaly removal intensity according to a set time gradient, the data screening behavior forms a controllable transition state in the time dimension. This ensures operational efficiency while maintaining the temporal integrity of the risk statistical samples, and improves the responsiveness of the pediatric oral health prevention and intervention assessment results to changes in population risk.

[0044] This invention provides, for example Figure 2 The health and medical big data-supported pediatric oral health prevention and intervention assessment system shown includes a concurrent rhythm construction module, a sample fluctuation analysis module, a distortion identification module, an abnormal interval location module, and a rhythm control module. The concurrency rhythm construction module collects the data upload time series and anomaly removal trigger records formed during the high concurrency phase, aligns them according to a unified time scale, constructs a concurrency rhythm graph, and calculates the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. The sample fluctuation analysis module performs synchronous statistics on the data upload time series around the anomaly trigger frequency curve, calculates the number of valid samples per unit time, forms a sample number change curve, and generates anomaly trigger expansion markers at the time positions corresponding to the rising anomaly trigger frequency. The distortion identification module uses anomaly-triggered expansion markers to compare the sample number change curve in segments, identify time segments in which the number of effective samples decreases and the risk curve does not change synchronously, extract risk distortion segments, and generate anomaly removal distortion records. The abnormal interval positioning module traces back the abnormal trigger frequency curve based on the abnormal removal distortion record, locates the time range corresponding to the rising section of the abnormal trigger frequency, determines the expanded interval of the abnormal removal range, and forms the adjustment time entry point. The rhythm control module dynamically controls the rhythm of the anomaly removal process around the adjustment time entry point. Before the anomaly trigger frequency rises, the removed data is replayed. During the anomaly trigger frequency rises, the anomaly removal intensity is reduced and the data is released in segments. During the anomaly trigger frequency recovery segment, the anomaly removal intensity is restored according to the set time gradient, thus realizing the time rearrangement adjustment of the anomaly removal process.

[0045] The health and medical big data-supported pediatric oral prevention and intervention assessment method provided in this embodiment of the invention is implemented through the aforementioned health and medical big data-supported pediatric oral prevention and intervention assessment system. For details of the specific methods and procedures of the health and medical big data-supported pediatric oral prevention and intervention assessment system, please refer to the embodiments of the aforementioned health and medical big data-supported pediatric oral prevention and intervention assessment method, which will not be repeated here.

[0046] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care, characterized in that, Includes the following steps: Collect the data upload time series and anomaly removal trigger records generated during the high-concurrency phase, align them according to a unified time scale, construct a concurrency rhythm map, and calculate the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. Synchronous statistics are performed on the data upload time series around the anomaly trigger frequency curve to calculate the number of valid samples per unit time, forming a sample number change curve, and anomaly trigger expansion markers are generated at the time positions corresponding to the rising anomaly trigger frequency segment. By using anomaly-triggered expansion markers to compare the sample number change curves in segments, we can identify time segments in which the number of effective samples decreases and the risk curve does not change synchronously within a continuous time segment, extract risk distortion segments, and generate anomaly removal distortion records. Based on the anomaly removal distortion record, trace back the anomaly trigger frequency curve, locate the time range corresponding to the rising anomaly trigger frequency segment, determine the anomaly removal range expansion interval, and form the adjustment time entry point. The abnormal removal process is dynamically rhythmically controlled around the adjustment time entry point. Before the abnormal trigger frequency rises, the removed data is replayed. During the abnormal trigger frequency rises, the abnormal removal intensity is reduced and the data is released in segments. During the abnormal trigger frequency recovery segment, the abnormal removal intensity is restored according to the set time gradient.

2. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 1, characterized in that, The steps for forming the abnormal trigger frequency curve are as follows: Collect data upload behavior within a continuous time interval and write it into the data upload time series in the order of occurrence. At the same time, collect the abnormal removal trigger records within the same time interval and mark them with time. The data upload time series and the anomaly removal trigger records are calibrated using the same time base. The time unit is divided into fixed-length time scale units and the timestamps are consolidated to form a time series set under a unified time scale. Data upload behavior within each time scale unit is aggregated and statistically analyzed around a unified time scale to form a data upload distribution sequence. At the same time, anomaly removal trigger records within each time scale unit are statistically analyzed to form an anomaly trigger count sequence. The data upload distribution sequence and the anomaly trigger count sequence are then mapped to a concurrent rhythm diagram. The abnormal trigger frequency sequence is formed by calculating the unit time frequency of the abnormal trigger count sequence based on the concurrent rhythm diagram, and then the abnormal trigger frequency sequence is mapped to the abnormal trigger frequency curve.

3. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 2, characterized in that, The abnormal trigger frequency sequence is embedded into the corresponding time coordinate of the concurrent rhythm graph in chronological order around a unified time scale, and the abnormal trigger frequency values ​​in each time scale unit are arranged continuously to keep the abnormal trigger frequency curve and the data upload time series corresponding to each other under the same time scale framework.

4. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 2, characterized in that, The steps for synchronously statistically analyzing the data upload time series based on the anomaly trigger frequency curve are as follows: The data upload time series is mapped according to the time scale unit consistent with the anomaly trigger frequency curve. Within each time scale unit, the records that were not removed by the anomaly are summarized and statistically analyzed to form the number of valid samples per unit time. The effective sample count sequence is formed by arranging the effective sample counts in chronological order around the effective sample counts per unit time, and the effective sample count sequence is mapped to a sample count change curve, keeping the sample count change curve and the anomaly trigger frequency curve on the same time scale frame. The continuously increasing time scale unit around the abnormal trigger frequency curve is divided into segments to form the abnormal trigger frequency rising segment, and the abnormal trigger frequency rising segment is mapped to the time scale unit corresponding to the sample number change curve. Anomaly trigger expansion markers are generated around the rising segment of the anomaly trigger frequency at the corresponding time scale position of the sample number change curve. The anomaly trigger expansion markers cover the time range corresponding to the rising segment of the anomaly trigger frequency.

5. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 4, characterized in that, The abnormal trigger frequency rising segment is determined based on the increasing state of the abnormal trigger frequency within the continuous time scale unit. The abnormal trigger expansion mark is marked with time coverage according to the start and end time positions of the abnormal trigger frequency rising segment, and is embedded in the time scale system of the sample number change curve.

6. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 4, characterized in that, The steps for segment comparison of the sample quantity change curve using anomaly-triggered expansion markers are as follows: Extract the data corresponding to the sample number change curve around the time scale segment covered by the abnormal trigger expansion marker, form the segment sample number sequence, and identify the segment where the effective sample number decreases within a continuous time segment. Data on the time scale segment corresponding to the risk curve is extracted around the segment where the number of effective samples decreases, forming a segment risk value sequence and identifying the segment where the risk curve has not changed synchronously; The continuous time intervals that simultaneously satisfy the conditions of a decrease in the number of valid samples and no synchronous change in the risk curve are identified, and risk distortion segments are extracted and the start and end time positions are recorded. Information on time segments, segments where the number of effective samples decreases, and segments where the risk curve does not change synchronously is integrated around risk distortion segments to generate anomaly removal distortion records and store them according to a unified time scale.

7. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 6, characterized in that, The anomaly removal distortion record includes the start time position, end time position, change value of effective sample quantity and risk value corresponding to the risk distortion segment. The anomaly removal distortion record and the anomaly trigger expansion mark maintain the same time scale and are arranged in chronological order to form a continuous time segment record.

8. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 6, characterized in that, The steps for tracing back the anomaly trigger frequency curve based on the anomaly removal distortion records are as follows: Read the start and end time positions corresponding to the anomaly removal distortion records according to a unified time scale, and map the corresponding time segments to the anomaly trigger frequency curve to form a segment anomaly trigger frequency sequence. The abnormal trigger frequency sequence is used to identify time segments where the abnormal trigger frequency increases continuously to form abnormal trigger frequency rising segments, and the time range of abnormal trigger frequency rising segments is recorded. The time intervals corresponding to the rising frequency of anomalies and the distorted records of anomaly removal are overlaid to determine the time scale unit covered by the rising frequency of anomalies as the expanded range of anomaly removal. The starting and ending time positions of the intervals around the anomaly removal range are extracted to form adjustment time entry points, and a correspondence is established between the adjustment time entry points and the anomaly removal distortion records.

9. The method for assessing pediatric oral health prevention and intervention supported by big data in health and medical care according to claim 8, characterized in that, The steps for dynamically controlling the pace of the anomaly removal process around the adjustment time entry are as follows: Extract data records that have been removed from the abnormality before the abnormality trigger frequency rises in the time range corresponding to the adjustment time entry, and re-insert them into the data processing flow in the original time order to complete the playback process; The intensity of the anomaly removal process is adjusted around the time scale unit within the rising range of anomaly trigger frequency. The anomaly removal intensity is adjusted to be lower than the original level, and the data that has been anomaly removed is released in segments. The anomaly removal intensity is gradually restored according to a set time gradient around the anomaly trigger frequency recovery section, so that the anomaly removal intensity is consistent with the time range of the anomaly trigger frequency recovery section; The processing results before, during, and after the abnormal trigger frequency rises are integrated using a unified time scale to achieve time rearrangement and adjustment of the abnormal removal process.

10. A pediatric oral health prevention and intervention assessment system supported by health and medical big data, used to implement the pediatric oral health prevention and intervention assessment method supported by health and medical big data as described in any one of claims 9, characterized in that, It includes a concurrent rhythm construction module, a sample fluctuation analysis module, a distortion identification module, an abnormal interval location module, and a rhythm control module: The concurrency rhythm construction module collects the data upload time series and anomaly removal trigger records formed during the high concurrency phase, aligns them according to a unified time scale, constructs a concurrency rhythm graph, and calculates the anomaly trigger frequency per unit time to form an anomaly trigger frequency curve. The sample fluctuation analysis module performs synchronous statistics on the data upload time series around the anomaly trigger frequency curve, calculates the number of valid samples per unit time, forms a sample number change curve, and generates anomaly trigger expansion markers at the time positions corresponding to the rising anomaly trigger frequency. The distortion identification module uses anomaly-triggered expansion markers to compare the sample number change curve in segments, identify time segments in which the number of effective samples decreases and the risk curve does not change synchronously, extract risk distortion segments, and generate anomaly removal distortion records. The abnormal interval positioning module traces back the abnormal trigger frequency curve based on the abnormal removal distortion record, locates the time range corresponding to the rising section of the abnormal trigger frequency, determines the expanded interval of the abnormal removal range, and forms the adjustment time entry point. The rhythm control module dynamically controls the rhythm of the anomaly removal process around the adjustment time entry point. Before the anomaly trigger frequency rises, the removed data is replayed. During the anomaly trigger frequency rises, the anomaly removal intensity is reduced and the data is released in segments. During the anomaly trigger frequency recovery segment, the anomaly removal intensity is restored according to the set time gradient.

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