A multi-center assisted reproductive medical record quality control data standardization collaborative platform

Abstract

This invention relates to the field of medical information processing technology and discloses a standardized collaborative platform for quality control data of multi-center assisted reproductive medical records. The platform includes: an edge data perception module for extracting key quality control data and collecting physical environment metadata to form a laboratory environment fingerprint; a semantic anchor construction module for generating a probability density distribution function reflecting the data distribution pattern using kernel density estimation methods and concatenating it with the laboratory environment fingerprint to generate distributed semantic anchors; a temporal logic verification module for performing temporal causal constraint verification based on the directed acyclic graph of assisted reproductive clinical pathways, intercepting distributed semantic anchors with logical anomalies; and a global collaborative calibration module for generating a semantic bias compensation operator based on the deviation between the distributed semantic anchors and the global standard distribution, and driving the quality control rule snapshot update. This invention eliminates the statistical bias caused by differences in the physical environment, improving the alignment accuracy and collaborative robustness of heterogeneous medical record quality control data.

Description

Technical Field

[0001] This invention relates to the field of medical information processing technology, specifically to a standardized collaborative platform for quality control data of multi-center assisted reproductive medical records. Background Technology

[0002] Currently, clinical research and medical quality control in assisted reproductive technologies increasingly rely on multi-center collaboration. With the increasing informatization of reproductive medicine, centers are generating massive amounts of medical record data. Achieving cross-institutional data interconnection and standardized evaluation of quality control results has become a key means to improve clinical outcomes and ensure medical safety.

[0003] Regarding the aforementioned issues, existing data processing systems typically employ standardized data dictionaries. Clinical data is aggregated from various branch centers to a central database via interfaces. The system uses pre-defined hard-coded rules to validate fields. The data aggregation process primarily relies on ETL tools. This model achieves formal standardization through a unified data structure.

[0004] However, existing technologies have shortcomings. The microenvironments of laboratories at different centers vary significantly. Factors such as incubator parameters, gas source indicators, and reagent batches can cause systematic statistical bias. Conventional methods cannot quantify the impact of such physical environments on the distribution of quality control data. At the data validation level, existing logical audits only target single-point values. They lack temporal causal modeling of the embryonic developmental biological pathway. Implicit temporal inversions or time span anomalies in records are often overlooked. Multi-center quality control standards are static and difficult to adapt to semantic drift caused by hardware updates. The varying sample sizes across centers lead to significant statistical bias. Simple field mapping is insufficient to achieve substantial alignment of statistical dimensions.

[0005] Therefore, this invention provides a standardized collaborative platform for quality control data of multi-center assisted reproductive technology cases to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a standardized collaborative platform for quality control data of multi-center assisted reproductive technology (ART) cases, which solves the problems of heterogeneity and systematic statistical bias in quality control data of cases caused by differences in the physical environment of laboratories and inconsistencies in clinical pathway logic among existing multi-center ART collaborations.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A standardized collaborative platform for quality control data of multi-center assisted reproductive technology (ART) cases includes:

[0009] The edge data sensing module is used to extract key quality control data from the sub-center and simultaneously collect physical environment metadata, and associate them to form a laboratory environment fingerprint;

[0010] The semantic anchor construction module is used to receive the key quality control data and the laboratory environment fingerprint, generate a probability density distribution function that reflects the distribution pattern of the data using the kernel density estimation method, and concatenate the probability density distribution function with the laboratory environment fingerprint to generate distributed semantic anchors.

[0011] The temporal logic verification module is used to perform temporal causal constraint verification on the data sequence covered by the distributed semantic anchors based on the directed acyclic graph of the assisted reproductive clinical pathway, and to intercept distributed semantic anchors with logical abnormalities.

[0012] The global collaborative calibration module is used to generate a semantic bias compensation operator based on the mathematical deviation between the verified distributed semantic anchors and the global standard distribution, and to drive the update of the quality control rule snapshot based on the semantic drift monitoring results.

[0013] By adopting the above technical solutions, deep alignment and environmental bias compensation of heterogeneous medical record quality control data are achieved through edge-side multi-dimensional feature perception, distributed semantic feature modeling, and biological time-series-based logical auditing. Therefore, the robustness and standardization accuracy of multi-center collaborative quality control are improved.

[0014] Preferably, the edge data sensing module performs the following steps to construct the laboratory environment fingerprint: It collects physical environment metadata consisting of incubator parameters, gas source indicators, and reagent batches through a physical environment sensor interface; it calculates the normalization ratio between the original environmental factor values ​​in the physical environment metadata and their preset maximum and minimum thresholds using a weight calculation unit; and it maps the multidimensional environmental factors to a unified numerical space by weighted summation of the normalization ratio and the corresponding preset weight coefficients, thereby generating the laboratory environment fingerprint.

[0015] By adopting the above technical solution, it is possible to map discrete and dimensionless laboratory physical characteristics into characteristic quantities that quantitatively describe the state of the laboratory environment.

[0016] Preferably, the semantic anchor construction module performs the step of generating a probability density distribution function as follows: obtaining discrete sample points of the quality control indicators of the sub-center; placing a Gaussian kernel function at each discrete sample point; calculating the weighted average of the kernel functions of all discrete sample points according to the preset smoothing bandwidth parameter; and transforming the discrete medical records into a continuous probability density distribution function that reflects the statistical distribution characteristics of the sub-center data.

[0017] By adopting the above technical solution, statistical bias caused by differences in sample size among sub-centers is eliminated, and sensitive information is anonymized and covered while preserving the original data distribution characteristics.

[0018] Preferably, the step of the temporal logic verification module performing temporal causal constraint verification specifically includes: extracting the timestamp sequence of medical record data encapsulated in the distributed semantic anchor; mapping the timestamp sequence of medical record data to the node positions corresponding to the directed acyclic graph of the assisted reproductive clinical pathway, and verifying the timestamp difference between adjacent developmental stage nodes; verifying whether the occurrence order of medical record data conforms to the biological unidirectional causal law, and checking whether the time span between adjacent developmental stage nodes is within the preset time interval threshold range.

[0019] By adopting the above technical solution, the inherent biological constraints of embryonic development are used to perform pre-audit of data quality and intercept non-compliant samples caused by data entry errors or system failures.

[0020] Preferably, the step of the global collaborative calibration module in generating the semantic bias compensation operator specifically involves: using a dynamic protocol calibration engine to solve an optimization objective function that includes the laboratory environment fingerprint constraint term and the regularization term of the temporal causal constraint verification result, calculating the distance measure between the source distribution and the target distribution, thereby quantifying the semantic offset; and generating a linear or nonlinear transformation relationship from the local central feature space to the global standard feature space based on the transmission scheme matrix solved by the optimization objective function, thus forming the semantic bias compensation operator.

[0021] By adopting the above technical solution, and by introducing physical background constraints and time series regularization terms, the root causes of cross-center data heterogeneity are identified and precise compensation is performed.

[0022] Preferably, the step of the global collaborative calibration module to execute the driving quality control rule snapshot update is as follows: the semantic drift monitoring unit calculates the probability density distribution of clinical indicators in the current period and the degree of deviation of the laboratory environment fingerprint relative to the baseline period, and generates a semantic drift intensity judgment index; when the semantic drift intensity judgment index exceeds a preset threshold, a consensus mechanism is triggered to drive the dynamic protocol calibration engine to recalculate the semantic bias compensation operator; the updated semantic bias compensation operator, quality control logic interception operator and environmental calibration benchmark are integrated to generate a quality control rule snapshot with a version timestamp and execute network-wide distribution.

[0023] By adopting the above technical solutions, the quality control standards are automatically synchronized with the evolution of the clinical environment and hardware updates, ensuring the technical reliability during long-term operation.

[0024] Preferably, the edge data perception module performs the step of extracting key quality control data as follows: automatically identifying key clinical indicator features from the heterogeneous medical record system through a non-invasive data capture unit; calling a de-identification operator to perform de-identification processing on the non-technical identification information in the key clinical indicator features, retaining technical feature vectors including the number of retrieved oocytes, fertilization method, and embryo grading; and aligning the technical feature vectors with the synchronously collected physical environment metadata according to the time axis.

[0025] By adopting the above technical solution, it is ensured that standardized technical features with statistical analysis value can be extracted without changing the original medical record database structure.

[0026] Preferably, the semantic anchor construction module performs the following steps to generate distributed semantic anchors: defining the probability density distribution function as a data dimension feature and the laboratory environment fingerprint as a background constraint dimension feature through a tensor encapsulation unit; performing tensor synthesis on the data dimension feature and the background constraint dimension feature in the logical space to generate a high-dimensional logical identifier representing the clinical logical essence of the subcenter under environmental constraints, thereby completing the construction of the distributed semantic anchors.

[0027] By adopting the above technical solution, the coupled representation of clinical statistical distribution and laboratory physical environment status is realized, providing a standardized input benchmark for subsequent semantic calibration.

[0028] Preferably, the step of the timing logic verification module to intercept the distributed semantic anchors with logical anomalies is as follows: calling the clinical pathway timing consistency judgment function to perform a quantitative judgment on the compliance of the data flow, and obtaining the timing causal constraint verification result; when the timing causal constraint verification result determines that the causal order and time interval of all adjacent developmental stages are valid, uploading the distributed semantic anchors; when the timing causal constraint verification result determines that there is a timing inversion or an abnormal time span, starting the logic interception operator, terminating the distributed semantic anchors with logical anomalies from entering the subsequent global collaboration process.

[0029] By adopting the above technical solution, the semantic information entering the collaborative platform is ensured to have biological authenticity.

[0030] Preferably, the calibration steps performed by the global collaborative calibration module are as follows: when performing cross-center data statistics or multi-center joint quality control analysis, the dynamic protocol calibration engine is invoked; the semantic bias compensation operator generated for the source sub-center is applied to the key quality control data to perform real-time logical correction, thereby eliminating the systematic statistical bias caused by differences in the laboratory physical environment.

[0031] By adopting the above technical solutions, the synergy and substantive alignment of heterogeneous medical record quality control data at the global level are ensured.

[0032] This invention provides a standardized collaborative platform for quality control data of multi-center assisted reproductive technology (ART) cases. It has the following beneficial effects:

[0033] 1. This invention solves the problem of inconsistent data distribution caused by differences in sample size or collection standards among different sub-centers by generating a probability density distribution function using a kernel density estimation method and constructing distributed semantic anchors in conjunction with the laboratory environment fingerprint. The distributed semantic anchors achieve deep alignment of cross-center quality control data features while ensuring the standardization of statistical dimensions without disclosing the privacy of original medical records due to the use of probability distribution representation.

[0034] 2. This invention utilizes a directed acyclic graph of the assisted reproductive clinical pathway to perform temporal causal constraint verification on the distributed semantic anchors, thereby achieving biological logical consistency auditing of the medical data generation process. By intercepting non-compliant data with reversed time sequences or abnormal time spans, it ensures that the data sequences involved in the calculations within the collaborative platform all conform to clinical diagnosis and treatment principles, thus improving the reliability of conclusions from multi-center joint quality control from the source.

[0035] 3. This invention utilizes a dynamic protocol calibration engine to calculate the semantic bias compensation operator and, combined with semantic drift monitoring results, drives the updating of quality control rule snapshots. This invention eliminates systematic statistical bias caused by differences in the physical environments of different laboratories. The semantic bias compensation operator enables real-time logical correction of heterogeneous data, allowing quality control standards to automatically calibrate as the clinical environment evolves, ensuring the long-term robustness of cross-center collaborative processes. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the architecture of a multi-center assisted reproductive technology case quality control data standardization and collaboration platform according to an embodiment of the present invention;

[0037] Figure 2 This is a flowchart illustrating a distributed standardization method for integrating environmental fingerprints and timing logic constraints according to an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the simulation results of distributed semantic anchor alignment according to an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram showing the consistency of quality control rules before and after evolution, according to an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] See attached document Figure 1 This invention provides a standardized collaborative platform for quality control data of multi-center assisted reproductive medical records. The platform includes an edge data perception module, a semantic anchor construction module, a temporal logic verification module, and a global collaborative calibration module.

[0042] The edge data sensing module is deployed on the data node side of each branch center, establishing bidirectional communication connections with the hospital information system and electronic medical record system within the branch center through a standard data interface. This module is equipped with a non-invasive data capture unit, capable of extracting key quality control data without altering the original medical record database structure, and de-identifying patient privacy-related identifying information using preset de-identification operators. The edge data sensing module also integrates a physical environment sensor interface, which accesses real-time hardware monitoring data within the laboratory via a bus protocol or wireless sensor network. This data includes, but is not limited to, incubator carbon dioxide concentration, culture environment temperature, laboratory laminar flow pressure difference, and batch codes of culture medium reagents. These physical environment parameters are then correlated with medical record data for the corresponding time period to form a laboratory environmental fingerprint.

[0043] The semantic anchor construction module establishes a data interaction connection with the edge data perception module. This module receives anonymized medical record data and corresponding laboratory environment fingerprints transmitted from the edge data perception module. Internally, the semantic anchor construction module includes a statistical modeling unit. This unit uses kernel density estimation to model the sample spatial distribution of medical record quality control indicators for each sub-center, transforming discrete medical record records into continuous probability density distribution functions. This module further uses a tensor encapsulation unit to perform high-dimensional feature concatenation of the probability density distribution function and the laboratory environment fingerprints, generating distributed semantic anchors. These distributed semantic anchors are used to logically characterize the data feature distribution of the sub-center under specific physical environmental constraints, providing a reference benchmark for subsequent global standardization.

[0044] The temporal logic verification module communicates with the semantic anchor construction module. This module internally stores a directed acyclic graph (DAG) of the assisted reproductive clinical pathway. This DAG is pre-set with a strict sequence of developmental stages and time interval thresholds between each stage based on the biological laws of embryonic development. The temporal logic verification module performs compliance checks on the medical record data sequences covered by the distributed semantic anchors through a logic auditing unit. If it detects that the timestamp sequence of a set of data violates the biological developmental temporal logic from oocyte retrieval, fertilization, cleavage to blastocyst formation, or that the time intervals between stages exceed the pre-set time interval thresholds, the module will execute a logic interception command to prevent abnormal semantic anchors from entering the global collaborative process, thereby achieving substantive quality control at the data source.

[0045] The global collaborative calibration module establishes network communication connections with the timing logic verification modules on each sub-center node side. This module deploys a dynamic protocol calibration engine, responsible for receiving verified distributed semantic anchors uploaded by each sub-center. The global collaborative calibration module generates a semantic bias compensation operator by calculating the mathematical deviation between the distribution characteristics of each sub-center and the global standard distribution, and applies this operator to perform real-time calibration during cross-center data retrieval or statistical analysis. Furthermore, the global collaborative calibration module includes a consensus management unit, which continuously monitors the stability of the data distribution across centers. When the environmental fingerprint of a specific center changes or the data distribution drifts beyond a threshold, it triggers the collaborative review mechanism of the global nodes, enabling the dynamic updating and distribution of quality control rule snapshots.

[0046] See attached document Figure 2 The present invention provides a distributed standardization method integrating environmental fingerprints and temporal logic constraints, which may include:

[0047] S100, the edge data perception module performs multi-dimensional edge feature extraction and constructs a laboratory environment fingerprint (LEF). In this step, the edge data perception module accesses the medical record database of each sub-center node through a data interface, and extracts clinical feature vectors including the number of retrieved oocytes, fertilization method, and embryo grading according to preset quality control operators. Simultaneously, the edge data perception module calls the physical environment sensor interface to synchronously acquire incubator parameters, gas source indicators, and reagent batch information corresponding to the time the medical record data was generated. Based on preset weight mapping rules, the module transforms these physical environment parameters into a multi-dimensional laboratory environment fingerprint (LEF), thereby providing a physical background characterization for subsequent medical record data.

[0048] S200, the semantic anchor construction module combines the laboratory environment fingerprint (LEF) with kernel density estimation to construct a distributed semantic anchor DSA. In this step, the semantic anchor construction module receives the clinical feature vector and the laboratory environment fingerprint (LEF) output by the edge data perception module. This module calculates the probability distribution function of the medical record indicators of each sub-center in the sample space through the kernel density estimation unit to characterize the statistical distribution characteristics of the data of that center. Subsequently, the semantic anchor construction module fuses and encapsulates the probability distribution function with the laboratory environment fingerprint (LEF) through tensor concatenation operations to generate a distributed semantic anchor DSA. This distributed semantic anchor DSA serves as the logical code for the data of each sub-center, realizing a coupled representation of the essential meaning of the data and its generating environment.

[0049] S300, the temporal logic verification module performs temporal causal constraint verification (TCCV) based on the directed clinical pathway graph. In this step, the temporal logic verification module obtains the distributed semantic anchor (DSA) generated by the semantic anchor construction module and extracts the time series of medical record data contained therein. This module calls the internally stored directed acyclic graph of assisted reproductive clinical pathways and compares the occurrence timestamps of the medical record data with the preset biological developmental time series of the directed acyclic graph. The temporal logic verification module verifies whether the data flow conforms to the legal order from oocyte retrieval to embryo transfer by executing logical judgment instructions, and checks whether the time interval between adjacent developmental stages is within the preset threshold range. If the judgment result is inconsistent with biological logic, the module intercepts the corresponding distributed semantic anchor (DSA).

[0050] In S400, the global collaborative calibration module utilizes a dynamic protocol calibration engine to calculate semantic bias and perform global alignment. In this step, the global collaborative calibration module receives verified distributed semantic anchor DSAs uploaded by each sub-center via a network protocol. The dynamic protocol calibration engine within this module invokes the optimal transmission algorithm to calculate the mathematical deviation between the distributed semantic anchor DSAs of each sub-center and the global standard distribution. Based on the calculated deviation, the dynamic protocol calibration engine generates a semantic bias compensation operator, which includes correction coefficients for differences in laboratory environments and temporal distribution characteristics. When performing cross-center data statistics or quality control queries, the global collaborative calibration module applies this compensation operator to perform real-time logical correction of the original statistical data, thereby achieving substantial standardization of heterogeneous data from multiple centers.

[0051] In the S500, the global collaborative calibration module monitors semantic drift and drives the collaborative evolution of quality control rule snapshots. In this step, the global collaborative calibration module continuously tracks the changing trends of the distributed semantic anchor points (DSAs) of each sub-center and calculates their offset strength from the initial baseline. When a specific sub-center experiences a structural change in its laboratory environment fingerprint (LEF) due to hardware updates or clinical protocol adjustments, and the degree of data distribution drift exceeds a preset threshold, the global collaborative calibration module automatically triggers a consensus mechanism. This module drives each node to perform logical reviews, recalculates the semantic bias compensation operator based on the latest data distribution characteristics, and generates a snapshot file containing the updated quality control rules. The global collaborative calibration module distributes this snapshot file to all sub-center nodes, completing the dynamic iteration of the quality control standards.

[0052] This embodiment provides a specific implementation method for multidimensional edge feature extraction and laboratory environment fingerprint construction, which may include:

[0053] In step S100, the edge data perception module establishes a communication connection with the heterogeneous medical record system on the sub-center side through a preset clinical data interface and performs multi-dimensional edge feature extraction. This module has a built-in quality control operator extraction unit, which automatically identifies and extracts key clinical indicator features from the original medical records according to the quality control standards defined in the field of assisted reproduction. Key clinical indicator features include, but are not limited to, the total number of retrieved oocytes, the number of mature oocytes, the fertilization method, the number of fertilized eggs, the grade of cleavage-stage embryos, the blastocyst grade, and the clinical pregnancy outcome after embryo transfer. When extracting the above data, the edge data perception module performs de-identification processing on non-technical identifying information such as the patient's name, ID number, and home address using a locally configured de-identification strategy, retaining only technical features with statistical analysis value.

[0054] Simultaneously, the edge data perception module, through an integrated physical environment sensor interface, synchronously collects underlying physical environment metadata corresponding to the time period of the medical record generation. This physical environment metadata consists of incubator parameters, gas source indicators, and reagent batches. Specifically, incubator parameters include real-time values ​​of carbon dioxide and oxygen concentrations inside the incubator, and fluctuations in the incubation room temperature; gas source indicators include laminar flow pressure difference data and gas purity monitoring data from the laboratory gas supply system; and reagent batches include unique batch codes for embryo culture medium, vitrification fluid, and thawing fluid. The edge data perception module aligns the collected real-time physical environment data with the corresponding medical record data along a timeline, assigning a corresponding physical environment background attribute to each clinical feature vector.

[0055] After acquiring the raw environmental parameters, the edge data perception module performs normalization and weight mapping on multidimensional environmental factors through its internal weight calculation unit. Because different laboratory equipment has different dimensions, and different environmental factors have varying degrees of influence on embryo development quality, this module transforms the discrete, dimensionlessly variable raw environmental parameters into a standardized laboratory environmental fingerprint through environmental feature normalization and weight mapping formulas. The laboratory environmental fingerprint is a feature quantity that can quantitatively describe the state of the laboratory environment at a specific center during a specific time period; this feature quantity serves as the benchmark input for subsequent semantic calibration.

[0056] In this embodiment, the formula for environmental feature normalization and weight mapping is as follows:

[0057] ;

[0058] In the above formula, For laboratory environmental fingerprinting; These are the original environmental factor values; The preset maximum threshold; The preset minimum threshold; These are the weighting coefficients; This represents the total number of dimensions of environmental factors.

[0059] The edge data awareness module, through the aforementioned calculation process, reduces the dimensionality of complex laboratory physical characteristics and maps them to a unified numerical space. The laboratory environment fingerprint output by this module encompasses information on the systematic impact of hardware equipment, gas environment, and consumable batches on clinical data. The generated laboratory environment fingerprint serves as the environmental characteristic identifier for that sub-center within that time period, and is transmitted along with the clinical feature vector to the semantic anchor construction module for subsequent semantic distribution modeling. This extraction method based on physical environment awareness ensures that the standardization process not only focuses on the consistency of data results but also considers the physical differences at the data source, providing an objective background reference for cross-center quality control.

[0060] This embodiment provides a specific implementation method for distributed semantic anchor generation, which may include:

[0061] In step S200, the semantic anchor construction module receives the set of clinical feature vectors and laboratory environment fingerprints output by the edge data perception module. Due to the heterogeneity in sample size and value range of the medical record data at each sub-center node, the semantic anchor construction module first invokes its internal statistical modeling unit to perform non-parametric modeling on the extracted discrete clinical indicator features. This process aims to transform the isolated medical record records of each center into a continuous probability distribution that can characterize the substantive meaning of the data, thereby eliminating statistical bias caused by differences in sample size.

[0062] The semantic anchor construction module uses kernel density estimation to process clinical feature vectors. The modeling unit generates a probability density function reflecting the distribution of specific quality control indicators for the center by placing a Gaussian kernel function at each clinical indicator sample point and calculating the weighted average of the kernel functions for all sample points. In this way, medical records that were originally difficult to compare directly are mapped to feature representations in the function space, preserving the original statistical characteristics of the data while achieving anonymization and overlay of original sensitive information.

[0063] In this embodiment, the specific formula for calculating the probability density distribution based on the Gaussian kernel function is as follows:

[0064] ;

[0065] In the above formula, is the probability density function of clinical indicator characteristics; This represents the total number of samples in the clinical feature vector; The preset smoothing bandwidth parameter; These are the clinical indicator variables to be evaluated; For the first Clinical indicator observations of each sample.

[0066] After obtaining the probability density function, the semantic anchor construction module performs multi-dimensional feature concatenation and encapsulation through the tensor encapsulation unit. This unit uses the probability density function as the feature representation of the data dimension and simultaneously introduces the laboratory environment fingerprint generated in step S100 as the background constraint dimension. The tensor encapsulation unit performs tensor synthesis of the above-mentioned data dimension features and background dimension features in the logical space, ultimately generating distributed semantic anchors.

[0067] The generated distributed semantic anchor is a high-dimensional logical identifier that includes the statistical distribution of medical record data and the state of the laboratory physical environment at the time this distribution occurred. The distributed semantic anchor does not contain the original details of the medical record; instead, it characterizes the clinical logical essence of the sub-center node under specific environmental conditions through the coupling of mathematical features and environmental fingerprints. This anchor is then transmitted to the temporal logic verification module to perform compliance audits based on biological developmental pathways, and also provides a standardized input benchmark for the subsequent global collaborative calibration module to calculate semantic bias. This construction method ensures that the laboratory physical differences hidden behind the data can be explicitly expressed during distributed collaboration.

[0068] This embodiment provides a specific implementation method for clinical pathway temporal causality constraint verification, which may include:

[0069] In step S300, the temporal logic verification module obtains the distributed semantic anchors generated by the semantic anchor construction module. This module internally contains a directed acyclic graph (DAG) model based on the assisted reproductive technology clinical pathway. This model characterizes the inherent biological developmental logic and operational causal relationships within the human assisted reproductive technology process. Each node in the DAG represents a specific clinical operation stage or embryonic development stage, including oocyte retrieval, fertilization, cleavage observation, and blastocyst formation. The edges of the DAG represent the unidirectional sequential relationships between stages and the strict temporal constraints. The temporal logic verification module first extracts the timestamp sequence of medical record data encapsulated in the distributed semantic anchors and maps the time data of each feature point to the corresponding node position in the DAG.

[0070] The temporal logic verification module performs a validity comparison on the mapped timestamp sequence through its internal logic auditing unit. The assisted reproductive process follows an irreversible biological unidirectional causal law, and the time intervals between developmental nodes have clear biological threshold limitations. The logic auditing unit searches the preset developmental time windows stored in the directed acyclic graph and checks the time difference between adjacent operation nodes in the medical records item by item. If a temporal reversal is detected in the timestamp sequence, such as the fertilization record time being earlier than the oocyte retrieval record time, or the time span between adjacent developmental stages exceeding the threshold range allowed by biological constants, the temporal logic verification module will determine that the data set has a risk of logical violation.

[0071] The temporal logic verification module invokes the clinical pathway temporal consistency judgment function formula during the verification process to quantitatively determine the logical compliance of the medical record data flow. This formula calculates the overall compliance status of the distributed semantic anchor point by performing a logical multiplication operation on the causal sequence and time interval of all adjacent stages in the clinical pathway. This verification method can effectively identify and eliminate logical anomalies caused by data entry errors, system transmission failures, or non-standardized operations.

[0072] In this embodiment, the formula for the clinical pathway temporal consistency determination function is as follows:

[0073] ;

[0074] In the above formula, The result of the clinical pathway time sequence consistency assessment; This represents the total number of stages in the assisted reproductive clinical pathway. For the first The actual timestamps of each developmental stage; For the first The actual timestamps of each developmental stage; For the first The first developmental stage to the third The maximum permissible time interval between each stage is preset; This is an indicator function that takes the value 1 when the condition inside the parentheses is true, and 0 otherwise.

[0075] The temporal logic verification module executes interception or release commands on distributed semantic anchors based on the clinical pathway temporal consistency judgment results. When the judgment result is 1, it indicates that all medical records covered by the distributed semantic anchor conform to the preset biological developmental timeline and causal constraints. The temporal logic verification module marks it as logically compliant and uploads it to the global collaborative calibration module via the communication interface. When the judgment result is 0, it indicates that there is a logical violation or abnormal time span in the data sequence. The temporal logic verification module activates the logic interception operator to prevent the abnormal anchor from participating in the subsequent global semantic alignment process. By introducing hard temporal logic constraints in a distributed environment, this invention achieves pre-audit of the quality of multi-center medical record data, ensuring that the semantic information entering the collaborative platform has biological authenticity and consistency.

[0076] This embodiment provides a specific implementation method for global semantic alignment and offset compensation, which may include:

[0077] In step S400, the global collaborative calibration module receives distributed semantic anchors uploaded by each sub-center node via a network protocol, and all distributed semantic anchors have passed the compliance audit of the time-series logic verification module. The global collaborative calibration module is equipped with a dynamic protocol calibration engine, which aims to solve the data semantic bias problem caused by differences in hardware equipment, fluctuations in laboratory microenvironment, and non-standardized operating procedures among different assisted reproductive centers. The dynamic protocol calibration engine uses the distributed semantic anchors of each sub-center as the source distribution and the system's preset global standardized quality control distribution as the target distribution, and establishes a mapping relationship between the two in the probability measure space using optimal transport theory.

[0078] The global collaborative calibration module quantifies the semantic offset between centers by calculating the Wasserstein distance between the source and target distributions. When constructing the transmission scheme, the dynamic protocol calibration engine considers not only the geometric distance of clinical indicator features but also simultaneously incorporates laboratory environment fingerprints as physical background constraint factors and clinical pathway temporal consistency judgment results as temporal regularization terms. By solving an optimization objective function that includes environmental constraints and temporal penalties, the dynamic protocol calibration engine can identify the root causes of cross-center data heterogeneity and generate a semantic offset compensation operator for specific centers. This operator embodies a linear or nonlinear transformation relationship from the local center feature space to the global standard feature space.

[0079] In this embodiment, the distributed alignment optimization objective formula that incorporates environmental factors and time-series regularization terms is specifically as follows:

[0080] ;

[0081] In the above formula, Optimize the objective function value for distributed alignment; These are the elements of the transmission scheme matrix between the source and target distributions; These are the elements of the distance metric matrix between the source distribution sample points and the target distribution sample points; For the laboratory environment fingerprint of the source branch center; This is the global standard environment fingerprint for the target end; The result of the clinical pathway time sequence consistency assessment; These are the adjustment weighting coefficients for environmental factors; This is the adjustment weight coefficient for the time series regularization term.

[0082] The global collaborative calibration module generates a semantic offset compensation operator based on the solved transport scheme matrix. During cross-center data retrieval, multi-center joint quality control analysis, or global data aggregation operations, the module applies the semantic offset compensation operator to the original data features of each sub-center, performing real-time logical compensation and calibration. Through this alignment mechanism based on physical context and biological time-series constraints, the platform eliminates systematic statistical biases caused by fluctuations in incubator efficiency, gas source purity, and developmental timelines across centers, ensuring substantial standardization and collaborative consistency of heterogeneous medical record quality control data at the global level.

[0083] This embodiment provides a specific implementation method for consensus-driven quality control rule evolution, which may include:

[0084] In step S500, the global collaborative calibration module performs a consensus-driven quality control rule evolution operation. This step aims to address the data distribution evolution caused by equipment aging, technology iteration, or clinical guideline updates during the long-term operation of assisted reproductive laboratories. The global collaborative calibration module continuously tracks the distributed semantic anchor DSA uploaded by each sub-center through its internal semantic drift monitoring unit. The semantic drift monitoring unit compares the distributed semantic anchor DSA received in the current period with the baseline semantic anchor initially established by the system, extracting the morphological change characteristics of the probability distribution of clinical indicators and the numerical fluctuation characteristics of the laboratory environment fingerprint LEF.

[0085] During monitoring, the global collaborative calibration module specifically identifies structural changes caused by laboratory hardware updates or culture protocol adjustments. When a systematic shift occurs in the incubator parameters or reagent batch information collected by the edge data sensing module, the laboratory environmental fingerprint (LEF) will show a significant numerical jump. The global collaborative calibration module quantifies the severity of this change by calculating a semantic shift intensity index. This index comprehensively considers the drift distance of case statistical features and the degree of deviation of physical environmental parameters, and is the core basis for determining whether to trigger global quality control rule iteration.

[0086] In this embodiment, the formula for the semantic offset strength determination index is as follows:

[0087] ;

[0088] In the above formula, It serves as a semantic offset strength determination index; This is the probability density function of the clinical indicator characteristics within the current period; is the probability density function of clinical indicator characteristics within the baseline period; For the current cycle's laboratory environment fingerprint; Laboratory environment fingerprints for the baseline period; These are the preset data distribution weighting coefficients; The preset environmental offset weighting coefficient; These are the clinical indicator variables to be evaluated.

[0089] When the semantic offset strength determination index determined by the global collaborative calibration module exceeds the preset evolution threshold, the module automatically triggers the multi-center node consensus mechanism. The global collaborative calibration module sends a rule evolution request to all sub-center nodes and simultaneously distributes suggested parameters containing the latest environmental impact factors and temporal constraint weights. After each sub-center node verifies the parameters through its internal logic, the global collaborative calibration module drives the dynamic protocol calibration engine to recalculate the semantic offset compensation operator. After the calculation is completed, the global collaborative calibration module generates a quality control rule snapshot (CQR-Snapshot) containing the updated compensation coefficients, quality control logic interception operators, and environmental calibration benchmarks.

[0090] The global collaborative calibration module distributes the generated Quality Control Rule Snapshots (CQR-Snapshots) to the storage units of each sub-center node via the network, replacing the original old version rules. Each CQR-Snapshot has a unique version timestamp, ensuring that multiple centers use the same logical benchmark when performing data standardization. Through this dynamic evolution mechanism based on semantic shift monitoring, this invention achieves automatic synchronization between quality control standards and changes in the clinical environment, avoiding the accuracy degradation that may occur in static standardization methods when facing environmental drift, and ensuring the technical reliability of the multi-center collaborative platform during long-term operation.

[0091] Specific application examples:

[0092] This embodiment uses two representative centers (Center A is a large tertiary hospital, and Center B is a prefecture-level branch center) in a cross-regional assisted reproductive medical consortium as examples to verify the standardization effectiveness of the present invention.

[0093] Laboratory environment fingerprint construction (corresponding to S100): In the edge data sensing module of center A, set the carbon dioxide concentration of the incubator. The percentage is 5.1%, and its preset maximum threshold is [missing information]. It is 6.0%, the minimum threshold. The weighting coefficient of this factor is 4.0%. The value is set to 0.4. Based on the environmental feature normalization and weight mapping formula, the weighted result of a single environmental factor is 0.22. Combined with other dimension parameters, the final laboratory environmental fingerprint of Center A is calculated. The value is 0.762; while Center B, due to its outdated equipment and large fluctuations in gas supply, has a lower value. The calculation result is 0.685.

[0094] Distributed semantic anchor generation and verification (corresponding to S200-S300): The semantic anchor construction module models the fertilization rate index of center A. The sample size is set. =500, smooth bandwidth parameter =0.05, the probability density function is generated using the kernel density estimation formula. and with The semantic anchor points (DSAs) of center A are generated by concatenation. The distribution of center A is as follows: Figure 3 As shown by the solid line in the middle.

[0095] During the verification phase, a set of medical records from Center B showed: egg retrieval time The fertilization time is 08:00. The time is 14:00, with an interval of 6 hours. This interval falls within the preset threshold range. Within [0,8], the result of the clinical pathway temporal consistency determination function is 1, indicating that the determination logic is compliant.

[0096] Global semantic alignment simulation (for S400 and related systems) Figure 3 (See attached document) Figure 3 , Figure 3 The alignment process of distributed semantic anchors in the feature space is demonstrated. The original distribution of center B (shown by the dotted line) is significantly left-skewed and has a reduced peak value compared to the global standard distribution due to the bias of the laboratory physical environment.

[0097] Global Co-calibration Module Setting Adjustment Weights =0.3, =0.2, and the objective function is solved using a dynamic protocol calibration engine. After calibration, the peak position and distribution shape of the central B distribution (shown by the dashed line) are highly aligned with the global standard distribution (shown by the solid line) after being corrected by the semantic bias compensation operator.

[0098] Evolution and Effect Comparison of Quality Control Rules (corresponding to S500 and its appendices) Figure 4 When center A updated its culture system, the monitoring unit detected that... Jump to 0.845. Set data weights. =0.6, =0.4, the semantic offset strength determination index is calculated. The value is 0.89, exceeding the threshold of 0.5. The system automatically generates and distributes a CQR-Snapshot containing the new correction factor.

[0099] See attached document Figure 4 , Figure 4 The performance of the method of this invention and conventional standardized methods under environmental interference is demonstrated.

[0100] Horizontal axis: Intensity of laboratory environmental differences (0.1-0.8).

[0101] Vertical axis: Standardized consistency score (out of 100).

[0102] Experimental results show that as the intensity of environmental differences increases, the consistency score of the conventional standardization method rapidly decreases from 90 to 55 (hollow circular curve); while the method of this invention, through LEF coupling and dynamic evolution, consistently maintains a score above 92 (solid square curve). This demonstrates that the present invention has high robustness and standardization accuracy when processing heterogeneous data.

Claims

1. A standardized collaborative platform for quality control data of multi-center assisted reproductive technology (ART) cases, characterized in that, include: The edge data sensing module is used to extract key quality control data from the sub-center and simultaneously collect physical environment metadata, and associate them to form a laboratory environment fingerprint; The semantic anchor construction module is used to receive the key quality control data and the laboratory environment fingerprint, generate a probability density distribution function that reflects the distribution pattern of the data using the kernel density estimation method, and concatenate the probability density distribution function with the laboratory environment fingerprint to generate distributed semantic anchors. The temporal logic verification module is used to perform temporal causal constraint verification on the data sequence covered by the distributed semantic anchors based on the directed acyclic graph of the assisted reproductive clinical pathway, and to intercept distributed semantic anchors with logical abnormalities. The global collaborative calibration module is used to generate a semantic bias compensation operator based on the mathematical deviation between the verified distributed semantic anchors and the global standard distribution, and to drive the update of the quality control rule snapshot based on the semantic drift monitoring results.

2. The standardized collaborative platform for quality control data of multi-center assisted reproductive technology cases according to claim 1, characterized in that, The specific steps for the edge data sensing module to construct the laboratory environment fingerprint are as follows: Physical environment metadata, consisting of incubator parameters, gas source indicators, and reagent batches, is collected through the physical environment sensor interface. The weight calculation unit is used to calculate the normalization ratio between the original environmental factor values ​​in the physical environment metadata and their preset maximum and minimum thresholds. By weighting and summing the normalization ratio and the corresponding preset weight coefficients, the multidimensional environmental factors are mapped to a unified numerical space, thereby generating the laboratory environmental fingerprint.

3. The standardized collaborative platform for quality control data of multi-center assisted reproductive technology cases according to claim 1, characterized in that, The semantic anchor construction module performs the following steps to generate the probability density distribution function: Obtain discrete sample points of the quality control indicators of the sub-center; A Gaussian kernel function is placed at each of the discrete sample points, and the weighted average of the kernel functions of all the discrete sample points is calculated according to the preset smoothing bandwidth parameter, so as to transform the discrete medical records into a continuous probability density distribution function that reflects the statistical distribution characteristics of the subcenter data.

4. The standardized collaborative platform for quality control data of multi-center assisted reproductive medical records according to claim 1, characterized in that, The specific steps for the timing logic verification module to perform timing causality constraint verification are as follows: Extract the timestamp sequence of medical record data encapsulated in the distributed semantic anchors; The timestamp sequence of the medical record data is mapped to the corresponding node position in the directed acyclic graph of the assisted reproductive clinical pathway, and the timestamp difference between adjacent developmental stage nodes is checked. Verify whether the order of occurrence of medical record data conforms to the biological unidirectional causal law, and check whether the time span between adjacent developmental stage nodes is within the preset time interval threshold range.

5. The standardized collaborative platform for quality control data of multi-center assisted reproductive medical records according to claim 1, characterized in that, The specific steps of the global collaborative calibration module in generating the semantic bias compensation operator are as follows: The semantic offset is quantified by using a dynamic protocol calibration engine to solve an optimization objective function that includes the laboratory environment fingerprint constraint term and the regularization term of the temporal causal constraint verification result. Based on the transmission scheme matrix obtained from the optimization objective function, a linear or nonlinear transformation relationship is generated to transform the local central feature space into the global standard feature space, forming the semantic bias compensation operator.

6. The standardized collaborative platform for quality control data of multi-center assisted reproductive medical records according to claim 1, characterized in that, The specific steps for the global collaborative calibration module to perform the snapshot update of the driving quality control rules are as follows: The semantic drift monitoring unit calculates the probability density distribution of clinical indicators in the current period and the degree of deviation of the laboratory environment fingerprint relative to the baseline period, and generates a semantic drift intensity judgment index. When the semantic offset strength determination index exceeds a preset threshold, a consensus mechanism is triggered, driving the dynamic protocol calibration engine to recalculate the semantic offset compensation operator. The updated semantic bias compensation operator, quality control logic interception operator, and environmental calibration benchmark are integrated to generate a quality control rule snapshot with a version timestamp and then distributed across the entire network.

7. The standardized collaborative platform for quality control data of multi-center assisted reproductive technology cases according to claim 1, characterized in that, The specific steps for the edge data sensing module to extract key quality control data are as follows: Key clinical indicators are automatically identified from heterogeneous medical record systems using a non-invasive data capture unit. The desensitization operator is invoked to perform de-identification processing on the non-technical identifier information in the key clinical indicator features, retaining the technical feature vectors including the number of retrieved oocytes, fertilization method, and embryo grading; The technical feature vectors are aligned with the synchronously acquired physical environment metadata according to the timeline.

8. The standardized collaborative platform for quality control data of multi-center assisted reproductive medical records according to claim 1, characterized in that, The semantic anchor construction module performs the following steps to generate distributed semantic anchors: The probability density distribution function is defined as a data dimension feature by using a tensor encapsulation unit, and the laboratory environment fingerprint is defined as a background constraint dimension feature. Within the logical space, tensor synthesis is performed on the data dimension features and the background constraint dimension features to generate a high-dimensional logical identifier that represents the clinical logical essence of the subcenter under environmental constraints, thereby completing the construction of the distributed semantic anchor.

9. The standardized collaborative platform for quality control data of multi-center assisted reproductive technology cases according to claim 1, characterized in that, The specific steps of the timing logic verification module in executing the distributed semantic anchor for intercepting logical anomalies are as follows: The clinical pathway temporal consistency determination function is invoked to perform a quantitative determination of data flow compliance, and the temporal causal constraint verification result is obtained. When the temporal causal constraint verification result determines that the causal order and time interval of all adjacent developmental stages are valid, the distributed semantic anchor is uploaded. When the temporal causal constraint verification result determines that there is a temporal inversion or an abnormal time span, the logical interception operator is activated to terminate the distributed semantic anchor of the logical anomaly and enter the subsequent global collaboration process.

10. A standardized collaborative platform for quality control data of multi-center assisted reproductive medical records according to claim 1, characterized in that, The specific steps for the global collaborative calibration module to perform calibration are as follows: When performing cross-center data statistics or multi-center joint quality control analysis, the dynamic protocol calibration engine is invoked. The semantic bias compensation operator generated for the source sub-center is applied to the key quality control data to perform real-time logical correction, thereby eliminating the systematic statistical bias caused by differences in the laboratory physical environment.

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