Kidney health assessment system fusing physiological parameter changes

Abstract

The invention relates to the technical field of medical data processing and storage, and discloses a kidney health assessment system fused with physiological parameter changes, comprising: a data stream access module accessing a discrete time sequence measurement data stream; the quantization control module operates random walk suppression logic based on anchor point adsorption, executes alternative logic gating operation, generates a state transition instruction only when the absolute value of the deviation degree of a sampling value and an anchor point value exceeds a floating threshold value, and forcibly blocks write-in if the absolute value of the deviation degree exceeds the floating threshold value; according to the method, index expansion of non-saliency micro-fluctuation data is blocked in a physical write-in layer through a double-lag quantization mechanism, and the accumulative error random walk phenomenon in a linear time sequence is effectively eliminated; and establishing an efficient targeted retrieval path aiming at a specific state evolution mode.

Description

Technical Field

[0001] This invention belongs to the field of medical data processing and storage technology, and in particular relates to a kidney health assessment system that integrates changes in physiological parameters. Background Technology

[0002] In the current clinical monitoring and disease management system for acute and chronic kidney disease, in order to capture and assess the early signs of renal function decline, modern monitoring systems generally use multimodal biosensor networks to continuously and frequently sample patients' hemodynamic parameters and biochemical metabolic parameters. Currently, when processing such time-series data with high concurrency write characteristics, the mainstream technical approach in the industry is to use a storage architecture based on a log structure merging tree or a time-series database. Its core operating mechanism follows the linear append logic of the time dimension. That is, the system mechanically packages all sensor readings at the same moment according to the sampling clock and persists them to a continuous physical address block on the disk through sequential I / O operations, thereby ensuring the integrity of the original data stream and the time-series traceability.

[0003] However, linear storage models are limited by the I / O throughput bottleneck of the storage medium, and their control logic also has flaws. They focus on macro-level result analysis but neglect the screening of underlying signal features. For example, Chinese invention patent CN119207750B discloses an intelligent operation management system and method for medical devices. It outputs evaluation results through a load prediction model and a health assessment model, and performs logical mapping of storage objects based on an operation management tree. This solution achieves automated management of device status. However, when faced with high-frequency sampling and inverted signal-to-noise ratio renal health monitoring data streams, the logical organization at the result layer does not delve into the physical layer quantization gating before data is written to disk. The uneven distribution of clinical value of physiological parameters and the inability of existing solutions to suppress random walk phenomena caused by sensor noise lead to low information entropy data at the stable baseline penetrating the storage link, causing non-significant expansion of index entries over time, incurring high I / O costs, and backtracking. Specific trends require full scanning, which limits the real-time response of the monitoring system. When this general linear storage method is applied to the dynamic assessment of kidney health with pathological feature identification as the core, a profound mismatch between its underlying data organization structure and the upper-level clinical retrieval needs is revealed. In engineering practice, the clinical value density of physiological parameters is not uniformly distributed over time, but is highly concentrated in specific change segments that characterize the transition of pathological states. The existing linear append mode causes the storage subsystem to record massive amounts of low information entropy data near a stable baseline indiscriminately. This data stacking causes the assessment system to pay a high I / O cost when performing retrieval tasks for specific pathological trends: in order to locate a few key abnormal fluctuations, the computing unit is forced to perform a full scan and decompression of the entire time window containing a large amount of invalid data, which causes a serious read amplification effect and limits the real-time response capability of the monitoring system.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a method that can decouple data sampling frequency and index generation density from the writing source, directly suppress noise and extract effective pathological change features at the physical storage level, thereby achieving real-time perception of complex multidimensional trends while significantly reducing storage and I / O overhead. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A kidney health assessment system integrating changes in physiological parameters, comprising: The data stream access module is used to access discrete time-series metric data streams that are arranged in time sequence and represent the state vectors of the monitored objects. The quantization control module is equipped with an anchor point state maintenance unit and a threshold dynamic configuration unit. It runs random walk suppression logic based on anchor point adsorption, and compares the current sampled value in the discrete time-series metric data stream with the current anchor point value in the anchor point state maintenance unit in real time. It is configured to perform a selective logic gating operation: when the absolute value of the deviation between the two exceeds the current critical range defined by the threshold dynamic configuration unit, a semantic state transition instruction is generated and the current anchor point value is updated using the current sampled value; otherwise, a write operation is forcibly blocked to maintain the current anchor point value unchanged. The index building module, in response to the triggering of semantic state transition instructions, extracts the timestamp of the transition and the state change type, and generates independent differential event index entries. The storage management module constructs a stable period log area and a mutation feature sparse index area with isolated storage space. Logical pointers are established based on differential event index entries and written into the mutation feature sparse index area to form an inverted index chain that maps state mutation features. This blocks the index expansion of non-significant micro-fluctuation data at the data persistence layer and establishes a targeted retrieval path for specific state evolution patterns.

[0006] Preferably, the quantization control module includes an embedded adaptive drift determination logic submodule, which performs the following quantization determination operation based on double inequality constraints: ,in, This is the current sampled value. This is the current anchor value. To characterize the preset basic threshold for effective state changes, This is the real-time background noise compensation coefficient calculated based on the statistical characteristics of historical data. This submodule only confirms the validity of the semantic state transition instruction when the above inequality holds, thereby achieving dual filtering of inherent jitter and non-trend random fluctuations of the data acquisition source.

[0007] Preferably, the anchor point state holding unit is configured to execute a non-linear lazy update strategy, that is, to keep the current anchor point value locked during the write operation blocking execution, and to force the subsequent sampled continuous data points to converge to the static anchor point in logical calculation until a new valid transition event occurs; the strategy makes the data stored in the storage management module exhibit a stepped discrete energy level distribution characteristic, eliminating the cumulative error random walk phenomenon in linear time series.

[0008] Preferably, the storage management module adopts a non-continuous read architecture driven by sparse indexes. When performing a retrieval task for a specific state trend, this architecture only scans the differential event index entries in the sparse index area of ​​mutation features, and directly locates and extracts the corresponding valid data segments in the log area of ​​the stationary period according to the logical pointer, skipping all background data blocks that have not undergone state transitions. This reduces the time complexity of retrieval from a linear level related to the total amount of data to a constant level related to the number of transition events.

[0009] Preferably, the threshold dynamic configuration unit is connected to a baseline drift compensation submodule. This submodule dynamically adjusts the value of the critical range in response to changes in the first-order statistical moments of the discrete time-series metric data stream. When a monotonic baseline drift trend is detected in the data stream, the submodule automatically increases the threshold for reverse transitions and decreases the threshold for positive transitions according to a preset reverse linear correction rule, so as to maintain sensitivity to reverse state reversion events while suppressing baseline noise.

[0010] Preferably, the index building module includes a multidimensional feature orthogonal mapping submodule for processing multidimensional concurrent data including serum creatinine, blood urea nitrogen, and urine flow rate. This submodule establishes independent and time-axis aligned differential index channels for the component data of the discrete time-series metric data stream of each dimension, and generates cross-dimensional event association pointers in the sparse index region of mutation features to support synchronous association retrieval of concurrent abnormal patterns of multidimensional parameters.

[0011] Preferably, the system also includes an early warning generation module, which is directly associated with the sparse index area of ​​mutation features and monitors the generation frequency of differential event index entries in real time. This module is set to trigger bypass switching logic to bypass the normal log writing process and output a high-priority status intervention alarm signal when the number of index entries generated within a unit time window exceeds a preset safety density threshold, or when the slope of parameter change indicated by continuously generated index entries exceeds a preset critical value.

[0012] Preferably, the storage management module adopts a hierarchical storage strategy at the logical level of the storage medium, mapping the sparse index area with mutation characteristics to the low-latency high-speed random access medium area, while mapping the log area during the stable period to the high-capacity sequential write medium area; the quantization control module reduces the write amplification factor for the high-speed storage medium area by significantly reducing the number of entries written to the sparse index area, and extends the lifespan of the medium.

[0013] Preferably, the data stream access module has a built-in data preprocessing submodule, which is used to perform time axis alignment and missing value filling on discrete data points from data acquisition sources with different sampling frequencies before the discrete time series measurement data stream enters the quantization control module, to ensure the comparability of the current sampled value and the current anchor value in the time dimension, and to eliminate false transition misjudgments caused by asynchronous sampling.

[0014] Preferably, the system also includes a data backtracking and reconstruction module. This module uses the anchor sequence and transition timestamp stored in the sparse index area of ​​mutation features, combined with a preset interpolation algorithm, to quickly reconstruct an approximate fitting curve that reflects the historical change trend of the object's state without having to completely read the log area of ​​the stationary period, and outputs it to the visualization output interface for the generation of state trend maps.

[0015] Compared to existing technologies, the renal health assessment system of this invention, which integrates changes in physiological parameters, has the following advantages: 1. In the kidney health assessment system, a sparse writing mechanism is established by constructing a quantization gating unit containing a discrete anchor register and an asymmetric hysteresis interval in the data writing path. This mechanism only responds to state transitions with potential energy breakthrough characteristics. The quantization grid is used to physically absorb the micro-oscillations and edge jitters that are common in the sensor data stream, locking the anchor update at the preset quantization step size grid. This hysteresis processing logic based on energy dissipation blocks the propagation of invalid fluctuations to the index storage area at the source, so that the generated index file only records energy level transition events with significant physical meaning. Thus, without sacrificing the ability to capture key trend features, the system eliminates index fragmentation and redundant writing caused by signal noise, ensuring high signal-to-noise ratio and compactness of index data in the storage medium, and solving the index bloat problem that is common in high-frequency time-series data storage.

[0016] 2. By configuring a synchronous bit-field synthesis unit in the write-time feature quantization processor, the bit width of strongly correlated multiple physiological parameter status codes within the same time window is aligned and orthogonally allocated. Bit operations are then used to concatenate them to generate a unique composite semantic key. The independent index scan and the intersection operation of a massive address list that traditional databases need to perform during the query phase are pre-emptively internalized into a single hash mapping action during the write phase. As a result, the system achieves semantic clustering of multi-dimensional features at the physical storage level, transforming the retrieval operation for complex association patterns into direct addressing of specific composite keys and O / 1 level complexity reading. This topological solidification of the data organization structure avoids the query engine from performing ineffective traversal and calculation of a large number of discrete pointer lists during runtime, reducing system latency and memory resource consumption during multi-parameter joint analysis.

[0017] 3. In addition to the instantaneous difference calculation path, a drift anchoring logic based on potential energy accumulation is set up in parallel. Only when the cumulative deviation between real-time data and dynamic anchor points exceeds a preset potential energy threshold is the anchor point reset triggered and an independent drift event index generated. This logic breaks the dependence of traditional time-series storage schemes on fixed sampling frequencies or short window baselines, and establishes a non-uniform sampling model in which the index generation density is proportional to the cumulative amount of data change. This enables the system to accurately capture and index long-period drift characteristics with extremely low change rates but continuous monotonicity across any time span. As a result, the system has the ability to passively perceive implicit long-period trends without having to perform full backtracking of historical data for integration calculation, thus achieving complete decoupling of storage resource consumption and monitoring time span.

[0018] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0019] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a logical diagram of the overall data flow and storage architecture of the system of this invention; Figure 2 This is a diagram of the internal interaction and judgment mechanism of the quantitative control module of this invention. Detailed Implementation

[0020] A kidney health assessment system that integrates changes in physiological parameters includes: The data stream access module is used to access discrete time-series metric data streams that are arranged in time sequence and represent the state vectors of the monitored objects. The quantization control module is equipped with an anchor point state maintenance unit and a threshold dynamic configuration unit. It runs random walk suppression logic based on anchor point adsorption, and compares the current sampled value in the discrete time-series metric data stream with the current anchor point value in the anchor point state maintenance unit in real time. It is configured to perform a selective logic gating operation: when the absolute value of the deviation between the two exceeds the current critical range defined by the threshold dynamic configuration unit, a semantic state transition instruction is generated and the current anchor point value is updated using the current sampled value; otherwise, a write operation is forcibly blocked to maintain the current anchor point value unchanged. The index building module, in response to the triggering of semantic state transition instructions, extracts the timestamp of the transition and the state change type, and generates independent differential event index entries. The storage management module constructs a stable period log area and a sparse index area with mutation features that are isolated in storage space. Logical pointers are established based on differential event index entries and written into the sparse index area with mutation features to form an inverted index chain that maps state mutation features. This blocks the index expansion of non-significant micro-fluctuation data at the data persistence layer and establishes a targeted retrieval path for specific state evolution patterns.

[0021] Preferably, the quantization control module includes an embedded adaptive drift determination logic submodule, which performs the following quantization determination operation based on double inequality constraints: ,in, This is the current sampled value. This is the current anchor value. To characterize the preset basic threshold for effective state changes, This is the real-time background noise compensation coefficient calculated based on the statistical characteristics of historical data. This submodule only confirms the validity of the semantic state transition instruction when the above inequality holds, thereby achieving dual filtering of inherent jitter and non-trend random fluctuations of the data acquisition source.

[0022] Preferably, the anchor point state holding unit is configured to execute a non-linear lazy update strategy, that is, to keep the current anchor point value locked during the write operation blocking execution, and to force the subsequent sampled continuous data points to converge to the static anchor point in logical calculation until a new valid transition event occurs; the strategy makes the data stored in the storage management module exhibit a stepped discrete energy level distribution characteristic, eliminating the cumulative error random walk phenomenon in linear time series.

[0023] Preferably, the storage management module adopts a non-continuous read architecture driven by sparse indexes. When performing a retrieval task for a specific state trend, this architecture only scans the differential event index entries in the sparse index area of ​​mutation features, and directly locates and extracts the corresponding valid data segments in the log area of ​​the stationary period according to the logical pointer, skipping all background data blocks that have not undergone state transitions. This reduces the time complexity of retrieval from a linear level related to the total amount of data to a constant level related to the number of transition events.

[0024] Preferably, the threshold dynamic configuration unit is connected to a baseline drift compensation submodule. This submodule dynamically adjusts the value of the critical range in response to changes in the first-order statistical moments of the discrete time-series metric data stream. When a monotonic baseline drift trend is detected in the data stream, the submodule automatically increases the threshold for reverse transitions and decreases the threshold for positive transitions according to a preset reverse linear correction rule, so as to maintain sensitivity to reverse state reversion events while suppressing baseline noise.

[0025] Preferably, the index building module includes a multidimensional feature orthogonal mapping submodule for processing multidimensional concurrent data including serum creatinine, blood urea nitrogen, and urine flow rate. This submodule establishes independent and time-axis aligned differential index channels for the component data of the discrete time-series metric data stream of each dimension, and generates cross-dimensional event association pointers in the sparse index region of mutation features to support synchronous association retrieval of concurrent abnormal patterns of multidimensional parameters.

[0026] Preferably, the system also includes an early warning generation module, which is directly associated with the sparse index area of ​​mutation features and monitors the generation frequency of differential event index entries in real time. This module is set to trigger bypass switching logic to bypass the normal log writing process and output a high-priority status intervention alarm signal when the number of index entries generated within a unit time window exceeds a preset safety density threshold, or when the slope of parameter change indicated by continuously generated index entries exceeds a preset critical value.

[0027] Preferably, the storage management module adopts a hierarchical storage strategy at the logical level of the storage medium, mapping the sparse index area with mutation characteristics to the low-latency high-speed random access medium area, while mapping the log area during the stable period to the high-capacity sequential write medium area; the quantization control module reduces the write amplification factor for the high-speed storage medium area by significantly reducing the number of entries written to the sparse index area, and extends the lifespan of the medium.

[0028] Preferably, the data stream access module has a built-in data preprocessing submodule, which is used to perform time axis alignment and missing value filling on discrete data points from data acquisition sources with different sampling frequencies before the discrete time series measurement data stream enters the quantization control module, to ensure the comparability of the current sampled value and the current anchor value in the time dimension, and to eliminate false transition misjudgments caused by asynchronous sampling.

[0029] Preferably, the system also includes a data backtracking and reconstruction module. This module uses the anchor sequence and transition timestamp stored in the sparse index area of ​​mutation features, combined with a preset interpolation algorithm, to quickly reconstruct an approximate fitting curve that reflects the historical change trend of the object's state without having to completely read the log area of ​​the stationary period, and outputs it to the visualization output interface for the generation of state trend maps.

[0030] Example 1: In high-concurrency storage scenarios for large-scale medical Internet of Things (IoMT) data, the system faces continuous write pressure from massive discrete time-series metric data streams from intensive care environments. This data stream exhibits typical high-frequency sampling characteristics, such as a sampling frequency of 1Hz and a significant signal-to-noise ratio inversion, meaning 99% of data points are within clinically insignificant physiological baseline oscillation ranges, superimposed with non-trendual random white noise from sensors. Under these conditions, if a traditional time-series database (TSDB) is used for full linear storage, the I / O throughput of the storage medium will be dominated by a large amount of invalid stationary period data. Furthermore, retrieval for specific pathological evolution patterns (such as continuously rising serum creatinine and decreased urine output) requires a full table scan, resulting in query response delays that cannot meet the needs of real-time risk assessment. The data stream access module performs a privacy-preserving preprocessing procedure on the incoming raw data packets. In this phase, the system performs linear range mapping for physiological parameters of different dimensions: serum creatinine values ​​in the range of 40umol / L to 1200umol / L and urine volume values ​​in the range of 0mL / h to 500mL / h are mapped to a 10-bit dimensionless integer range of 0 to 1023 through proportional scaling. The mapping logic is to subtract the minimum value of the range from the current sampled value, multiply by 1023, and then divide by the range span value. This step eliminates the difference in physical dimensions, ensuring that the subsequent quantitative control module can perform selective logic gating operations on a unified numerical scale. The system strips all metadata related to patient identity, retaining only the de-identified time-series numerical pairs and channel identifiers. This cleaned discrete time-series measurement data stream is pushed into the input buffer queue of the quantitative control module. Inside the quantitative control module, the system allocates a dedicated anchor state holding unit in the cache for each independent physiological parameter channel to hold the current logical anchor value. In the initial state, the system writes the first valid sampled value into this unit as a reference. When a new current sampled value is obtained... Upon arrival, the quantization control module does not directly persist the result, but instead initiates a random walk suppression logic based on anchor point adsorption. This logic calls preset judgment parameters in the threshold dynamic configuration unit, including the base threshold. and real-time background noise compensation coefficient The system performs core selectivity logic gating operations to calculate inequalities. The truth value of .

[0031] During this calculation, if the above inequality does not hold, meaning the deviation between the current sampled value and the anchor value is within the hysteresis dead zone defined by the threshold, the quantization control module determines that the current data is a non-significant micro-fluctuation or sensor noise. In this case, the system forcibly executes a write operation block, discards the current sampled data, and maintains the anchor state within the holding unit. Numerical locking eliminates small errors that accumulate over time and prevents anchor points from undergoing invalid random walks due to noise. Conversely, when a significant change with potential energy breakthrough characteristics occurs in the data stream, causing the above inequality to hold, the quantization control module confirms a valid trend change and generates a semantic state transition instruction. The system then uses the current sampled value... renew The system completes a discrete energy level transition. Responding to the semantic state transition instruction, the index construction module captures the timestamp of the transition and the state change type. For scenarios involving multi-dimensional feature association, the multi-dimensional feature orthogonal mapping submodule within the index construction module is activated synchronously. This submodule reads the state codes of the first and second physiological parameters within the same time window in parallel. The system performs a bit-domain synthesis operation, mapping the rising state code of serum creatinine to the high bit domain and the falling state code of urine volume to the low bit domain. A unique composite semantic key is generated through bitwise operations. The specific mapping protocol is as follows: a 64-bit integer is used as the composite key carrier. Bits 0 to 7 are defined as the serum creatinine state domain, where 01h represents rising, 02h represents falling, and 00h represents stable. Bits 8 to 15 are defined as the urine volume state domain, and bits 16 to 23 are defined as the mean arterial pressure state domain. The system shifts each channel state code into the corresponding bit domain using an 8-bit left shift instruction and then performs a bitwise OR operation to generate... The composite key is directly used as the hash address of the high-speed access medium to achieve risk pattern matching in a single instruction cycle. Based on the composite semantic key, the storage management module establishes a logical pointer to the physical address of the corresponding original data block in the sparse index area of ​​mutation features. During the retrieval phase, when the evaluation task needs to locate the composite risk pattern of elevated serum creatinine accompanied by decreased urine output, the storage management module no longer scans the massive stationary period log area, but instead addresses the differential event index entries with key-value matching in the sparse index area of ​​mutation features. Based on the logical pointer in the index entry, the corresponding valid data segment is extracted and reconstructed, avoiding the reading and decompression of all background data blocks that have not undergone state transitions. This achieves an order-of-magnitude reduction in storage space usage and reduces the retrieval time complexity for specific state evolution patterns from a linear level of O(N) to a constant level of O(K) that is only related to the number of transition events. This ensures a response and accurate backtracking to long-term, multi-dimensional complex trends under limited hardware resources.

[0032] Example 2: This example aims to verify the storage efficiency and retrieval performance of the proposed renal health assessment system, which integrates physiological parameter changes, when processing high-concurrency, high-noise discrete-time metric data streams by constructing an experimental platform simulating a real medical intensive care unit (ICU) data environment. It focuses on examining the impact of key technical parameters on the system's random walk suppression capability and sparse index construction effect. The core technical challenge addressed in this experiment is how to filter out high-frequency white noise and physiological micro-oscillations originating from sensors to the greatest extent possible while preserving clinically significant pathological mutation characteristics, thereby solving the index bloat and read amplification problems existing in traditional linear storage schemes. To reproduce these challenges, the experiment built a test platform based on a general-purpose x86 architecture server. The test environment was configured with a 16-core 3.2GHz processor and 64GB of memory. The storage medium was an enterprise-grade NVMe SSD. For the data source, a mixed data stream containing desensitized fragments of the MIMIC-IV public dataset was selected. This stream included sampling data from three key physiological channels: serum creatinine (SCr), hourly urine output (UrineOutput), and mean arterial pressure (MAP). To strictly comply with engineering realism protocols, the cleaned dataset was not used directly in the experiment. Instead, a Gaussian white noise with a signal-to-noise ratio (SNR) of 20dB was actively superimposed on the original data stream by a signal generator, and a 50Hz power frequency interference with an amplitude of 5% of the baseline value was introduced to simulate the output characteristics of real bedside monitoring equipment in a complex electromagnetic environment.

[0033] The experiment designed two sets of comparative experiments: one set was the control group, which used a traditional compression algorithm based on fixed dead band (such as the rotating door algorithm SDT); the other set was the sample group of this invention, which used the random walk suppression logic based on anchor point adsorption and the double hysteresis quantization mechanism of this invention. In the sample group of this invention, the basic threshold of the key parameter was... Set to 10% of the current anchor point value, real-time background noise compensation coefficient Based on the dynamic calculation of the sliding window variance of the preceding 100 sampling points, the core observation indicators of the experiment included: index compression ratio, write operation frequency, and retrieval response time for specific pathological patterns (such as serum creatinine increasing by >0.3 mg / dL within 48 hours). After the experiment started, both systems were simultaneously connected to a data stream with a sampling frequency of 1 Hz. In the first phase (0-24 hours), the data stream simulated the state of the patient's physiological indicators in a stable period. At this time, the signal mainly consisted of small fluctuations near the baseline and superimposed noise. Monitoring data showed that, due to the lack of dynamic suppression of non-trend noise, the write operation frequency of the control group remained at a high level (average about 0.2 times / second), and the volume of the generated index file increased linearly over time, indicating that a large amount of noise data penetrated the fixed dead zone and was recorded as valid events. In contrast, the write operation frequency of the sample group of this invention decreased to below 0.005 times / second during the stable period, and the index compression ratio reached more than 150:1, confirming the inequality in the quantization control module. The gating function: When the data fluctuation does not break through the dynamic potential energy barrier composed of the basic threshold and the noise compensation coefficient, the system blocks invalid write operations, locks the anchor point in a logical static state, and eliminates random walks caused by accumulated errors.

[0034] In the second phase (24-48 hours), pathological mutation events simulating acute kidney injury (AKI) were injected into the data stream, manifested as a monotonically increasing serum creatinine level by more than 50% within 6 hours. At this time, the sample group of this invention generated a series of dense semantic state transition instructions. The index building module quickly captured this trend change and generated corresponding differential event index entries in the sparse index region of mutation features. Simultaneously, the experiment introduced a problem intensity gradient control system, testing the system performance under different signal-to-noise ratios (10dB, 20dB, 30dB). The results showed that as the signal-to-noise ratio decreased (i.e., noise intensity increased), the false trigger rate of the control group increased exponentially, while the sample group of this invention, thanks to... With adaptive adjustment, the false trigger rate only showed a slight linear increase, and even under extreme conditions with a signal-to-noise ratio as low as 10dB, it could still maintain a 100% capture rate for pathological mutation events, while maintaining storage compression performance that was an order of magnitude better than the control group. The final retrieval performance test showed that when performing targeted retrieval for the above-mentioned AKI events, the sample group of this invention used the inverted index chain in the sparse index area of ​​mutation features, and only needed to scan less than 1% of the total data volume of index entries to locate the target data segment, with an average retrieval time of 15ms; while the control group, due to severe index expansion, needed to scan a large number of invalid indexes, with an average retrieval time of 420ms. This proves that in a real and complex engineering environment, the solution of this invention, through the synergistic effect of anchor point adsorption and dynamic threshold, achieves high-fidelity extraction of effective information and efficient suppression of invalid noise, achieving a dual qualitative leap in storage density and retrieval efficiency.

[0035] Example 3: This example provides a detailed engineering description of the adaptive parameter adjustment mechanism and multi-dimensional feature index construction logic within the quantization control module, eliminating uncertainties at the algorithm implementation level and ensuring the stability and reproducibility of the system when facing differences in the physiological baselines of different patients. It also addresses the real-time background noise compensation coefficient in the threshold dynamic configuration unit. For the calculation, the system adopts a weighted standard deviation estimation algorithm based on a sliding window. The system maintains a first-in-first-out queue of length W (recommended value is 50 to 100 sampling points) to store the sampled values ​​of the most recent W times. When a new sampled value is added... Upon arrival, the system calculates the mean of the data within that window. and standard deviation To leverage recent data with higher weighting for faster response to environmental changes, the system introduces a time decay factor λ (ranging from 0.9 to 0.99) and updates the noise baseline using an exponentially weighted moving average (EWMA) and real-time background noise compensation coefficient. The calculation follows the formula Where k is the signal-to-noise ratio adjustment gain, typically set between 2.0 and 3.0. This calculation process ensures that when ambient noise increases, Automatically increasing the potential energy barrier raises the potential energy barrier and suppresses ineffective random walks; conversely, in low-noise environments, This reduces and improves the system's sensitivity to capturing weak pathological signals.

[0036] For the inverse linear correction rule of the baseline drift compensation submodule, the system sets a long-term trend monitoring window (e.g., 1 hour). Within this window, the system calculates the cumulative sum of the first derivative (i.e., the rate of change) of the data stream in real time. When the absolute value of this cumulative sum exceeds the preset drift judgment threshold... When the system determines that a monotonic baseline drift exists, the baseline drift compensation submodule performs an asymmetric threshold correction operation: if a positive drift is detected (the cumulative sum is positive), the system reduces the threshold for determining a positive abrupt change. At the same time, the threshold for determining the reverse transition is increased. Correction amount The system exhibits a linear positive correlation with the drift rate, ensuring that even when a patient's physiological indicators slowly deteriorate, the system will not miss early pathological trends due to a fixed absolute threshold. This achieves zero-phase delay tracking of chronic disease characteristics. Regarding the bit-domain synthesis logic of the multidimensional feature orthogonal mapping submodule, the system uses a fixed-length 64-bit integer as the composite semantic key. The system predefines the bit offset and bit width of each physiological parameter channel in the composite key; for example, the serum creatinine channel is assigned to the lower 32 bits, and the urine output channel is assigned to the higher 32 bits. When the index building module receives the state transition instruction of the multidimensional parameter, it converts the physical value or state code of each channel into binary format. The system performs a bitwise left shift operation, moving the code of each channel to the corresponding bit-domain position, and performs a bitwise OR operation to merge all bit-domains, generating a unique composite semantic key. This process can be completed in just a few clock cycles at the CPU register level, improving the index building efficiency during multidimensional association queries and ensuring that the key values ​​stored in the sparse index area have global uniqueness and resolvability. In terms of configuration, the system follows the principle of maximizing signal-to-noise separation, and determines the parameters through parameter sensitivity analysis on offline datasets. The optimal value should be slightly higher than the peak-to-peak value of the sensor's inherent noise floor amplitude. In actual engineering deployments, the system executes a no-load idle self-test program to collect pure ambient noise data, calculates its 99.7th percentile (i.e., the 3σ principle), and sets this value as the initial value. This ensures that the system can automatically obtain the best silent reference on acquisition devices of different precision, and can adapt to diverse hardware environments without manual intervention.

[0037] Example 4: This example provides supplementary explanations regarding the initial calibration procedures and physical security strategies for data storage under different acquisition environments and individual differences, eliminating potential implementation obstacles and compliance risks, and ensuring the stability and reliability of the system throughout its entire lifecycle. Addressing the cold start issue when the system first connects to a new patient or when changing acquisition equipment, this invention designs a standardized on-site deployment pre-calibration procedure. Before formally activating the quantitative control module, the system enters a 24-hour baseline learning mode. In this mode, the system bypasses random walk suppression logic to perform lossless full acquisition of the raw data stream. Using the acquired full data, the system performs offline statistical analysis to calculate the physiological parameter distribution characteristics of the specific patient in the current equipment environment, including mean, standard deviation, and intraday fluctuation range. Based on these statistics, the system automatically initializes the anchor point state maintenance unit. The initial parameters of the threshold dynamic configuration unit are set according to the calculated background noise level. and This ensures that the system parameters are personalized and adapted, avoiding incompatibility caused by using general preset values, and guaranteeing the accuracy of subsequent quantitative control from the source.

[0038] To address the physical security and compliance issues of data storage, this invention incorporates a physical isolation and encrypted sharding mechanism into the underlying implementation of the storage management module. For the stable period log area containing sensitive physiological information, the system uses the AES-256 standard for full-disk encryption and distributes the encrypted data blocks across multiple physically isolated disk arrays. For the sparse index area containing only state change information, the system employs a lightweight hash verification mechanism to ensure data integrity. Furthermore, the system sets a strict data lifecycle management strategy. For historical data exceeding the legal retention period, such as 5 years, a physical destruction process based on secure erasure standards is automatically executed to ensure the forgetfulness of patient privacy data. This mechanism constructs a data security barrier at both physical and logical levels, meeting the compliance requirements of medical data management.

[0039] Example 5: This example discloses a standardized pre-deployment parameter calibration procedure for the system, ensuring that the quantitative control module can achieve accurate adaptation and optimal performance of key control parameters when facing heterogeneous acquisition environments and diverse patient groups in different medical institutions. This solves the problem of system performance fluctuations caused by a lack of objective basis for parameter settings and provides a deterministic operational path for engineering reproduction. The procedure defines the input conditions for parameter calibration: acquiring a historical discrete time-series metric data stream with a duration of no less than 24 hours, which includes typical physiological fluctuation characteristics of the target patient in resting and routine activity states. The system executes an offline analysis process based on statistical characteristics to calculate the data stream within a preset sliding window (e.g., window size). The system analyzes the local variance sequence within the noise region and plots a variance distribution histogram. It then identifies low variance peak regions in the histogram corresponding to background noise and uses the 95th percentile of these regions as the initial benchmark value for the real-time background noise compensation coefficient. For abrupt events in the data stream, the system uses a first-order difference operator to extract the absolute difference of all adjacent sampling points, and employs the K-Means clustering algorithm to divide the difference values ​​into noise clusters and signal clusters. The system selects the Euclidean distance between the cluster center of the noise cluster and the maximum boundary value as the set basic threshold. The lower limit is based on ensuring It can cover the vast majority of non-pathological random disturbances.

[0040] Building upon this, a validation feedback loop for parameter optimization is introduced. The system constructs a test set containing simulated pathological mutation signals (such as synthetic signals with a step amplitude of 20% of the baseline value) and optimizes the parameter space. and The system performs a grid search, using signal-to-noise ratio gain and response delay time as joint optimization objectives. It iterates through and calculates the system performance score under different parameter combinations. Finally, the procedure outputs a set of parameter tuples that maximize the overall score, which serves as the optimal operating point configuration for this specific deployment environment. This eliminates the arbitrariness of manual experience settings and ensures the stability and consistency of the system in the engineering environment.

[0041] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A kidney health assessment system integrating changes in physiological parameters, characterized in that, include: The data stream access module is used to access discrete time-series metric data streams that are arranged in time sequence and represent the state vectors of the monitored objects. The quantization control module is equipped with an anchor point state maintenance unit and a threshold dynamic configuration unit. It runs random walk suppression logic based on anchor point adsorption, and compares the current sampled value in the discrete time-series metric data stream with the current anchor point value in the anchor point state maintenance unit in real time. It is configured to perform a selective logic gating operation: when the absolute value of the deviation between the two exceeds the current critical range defined by the threshold dynamic configuration unit, a semantic state transition instruction is generated and the current anchor point value is updated using the current sampled value; otherwise, a write operation is forcibly blocked to maintain the current anchor point value unchanged. The index building module, in response to the triggering of semantic state transition instructions, extracts the timestamp of the transition and the state change type, and generates independent differential event index entries. The storage management module constructs a stable period log area and a mutation feature sparse index area with isolated storage space. Logical pointers are established based on differential event index entries and written into the mutation feature sparse index area to form an inverted index chain that maps state mutation features. This blocks the index expansion of non-significant micro-fluctuation data at the data persistence layer and establishes a targeted retrieval path for specific state evolution patterns.

2. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The quantization control module includes an embedded adaptive drift decision logic submodule, which performs the following quantization decision operation based on double inequality constraints: ,in, This is the current sampled value. This is the current anchor value. To characterize the preset basic threshold for effective state changes, This is the real-time background noise compensation coefficient calculated based on the statistical characteristics of historical data.

3. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The anchor point state holding unit is configured to execute a non-linear lazy update strategy, that is, to keep the current anchor point value locked during the write operation blocking execution, and to force the subsequent sampled continuous data points to converge to the static anchor point in logical computation until a new valid transition event occurs. The strategy makes the data stored in the storage management module exhibit a stepped discrete energy level distribution characteristic, eliminating the cumulative error random walk phenomenon in linear time series.

4. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The storage management module adopts a non-continuous read architecture driven by sparse indexes. When performing retrieval tasks for specific state trends, this architecture only scans the differential event index entries in the sparse index area of ​​mutation features, and directly locates and extracts the corresponding valid data segments in the log area of ​​the stationary period based on logical pointers, skipping all background data blocks that have not undergone state transitions.

5. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The threshold dynamic configuration unit is connected to a baseline drift compensation submodule. This submodule responds to the change of the first-order statistical moment of the discrete time-series metric data stream and dynamically adjusts the value of the critical range. When a monotonic baseline drift trend is detected in the data stream, the submodule automatically increases the threshold of the reverse jump and decreases the threshold of the positive jump according to the preset reverse linear correction rule.

6. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The index building module includes a multidimensional feature orthogonal mapping submodule for processing multidimensional concurrent data including serum creatinine, blood urea nitrogen, and urine flow rate. This submodule establishes independent and time-axis aligned differential index channels for the component data of the discrete time-series metric data stream for each dimension, and generates cross-dimensional event association pointers in the sparse index region of mutation features to support synchronous association retrieval of concurrent abnormal patterns of multidimensional parameters.

7. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The system also includes an early warning generation module, which is directly associated with the sparse index area of ​​mutation features and monitors the generation frequency of differential event index entries in real time. This module is set to trigger bypass switching logic to bypass the normal log writing process and output a high-priority status intervention alarm signal when the number of index entries generated within a unit time window exceeds a preset safety density threshold, or when the slope of parameter change indicated by continuously generated index entries exceeds a preset critical value.

8. The kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The storage management module adopts a hierarchical storage strategy at the logical level of the storage medium, mapping the sparse index area with mutation characteristics to the low-latency high-speed random access medium area, while mapping the log area during the stationary period to the high-capacity sequential write medium area; the quantization control module reduces the write amplification factor for the high-speed storage medium area by significantly reducing the number of entries written to the sparse index area, and extends the life of the medium.

9. A kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The data stream access module has a built-in data preprocessing submodule, which is used to perform time axis alignment and missing value filling on discrete data points from data acquisition sources with different sampling frequencies before the discrete time series metric data stream enters the quantization control module.

10. A kidney health assessment system integrating physiological parameter changes according to claim 1, characterized in that, The system also includes a data backtracking and reconstruction module. This module utilizes the anchor sequence and transition timestamps stored in the sparse index area of ​​mutation features, combined with a preset interpolation algorithm, to quickly reconstruct an approximate fitting curve that reflects the historical trend of object state changes without having to fully read the stationary period log area. The curve is then output to the visualization output interface for generating state trend maps.

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