An operation management system for hospital data governance

Abstract

This invention relates to the field of hospital data governance technology, specifically to an operation and management system for hospital data governance, comprising: a composite event data encapsulation module and a multimodal semantic alignment module, a dynamic stream processing synchronization module, and a load monitoring and permission scheduling module. This invention introduces a dynamic adaptive waterline scheduling model based on a directed acyclic graph causal chain and a hardware-level interrupt release mechanism, breaking the bottleneck of invalid memory residency caused by traditional fixed time windows. While ensuring strong consistency aggregation of concurrent data across nodes, it achieves immediate release of the underlying memory buffer pool and an exponential reduction in garbage collection overhead. Furthermore, it constructs an instantaneous load prediction calculus model that integrates an exponential forgetting mechanism and the integral of concurrent arrival rate, coupled with kernel-level warm data buffer redirection and socket-level asynchronous write thread strong locking control.

Description

Technical Field

[0001] This invention relates to the field of hospital data governance technology, and more specifically to an operation and management system for hospital data governance. Background Technology

[0002] With the widespread adoption of distributed computing architectures and IoT technologies, data access and consistency control across multiple heterogeneous terminals in concurrent scenarios has become a core underlying technology urgently needing breakthroughs in the field of big data processing. In conventional heterogeneous clusters, workstations on various business nodes typically push unstructured business data to a central processing platform for aggregation and computation in real time via application layer protocols. However, in actual industrial-grade high-throughput applications, existing heterogeneous data consistency control systems generally face the following serious underlying technical deficiencies: First, during the concurrent data access and cleaning phase, transmission jitter in the underlying physical network links and uncontrollable asynchronous drift of the physical clocks of distributed terminal nodes directly cause concurrent data packets arriving at the gateway to lose their global partial order, easily leading to severe timing errors in the downstream computing engine. Simultaneously, when mapping unstructured data to standard knowledge bases, existing technologies often rely on rigid string comparisons or shallow semantic similarity calculations. This coarse-grained alignment method ignores the spatial differences in the deep topological structures of complex knowledge graphs in specific domains. For data with similar textual semantics but belonging to different independent branches in the underlying directed acyclic graph tree structure, cross-level erroneous mappings are easily generated, thus contaminating the entire downstream computing base.

[0003] Second, in the concurrency control phase of stream processing, mainstream stream computing frameworks typically employ a watermark mechanism based on a fixed latency parameter when handling out-of-order data streams. Because this mechanism lacks awareness of the underlying causal dependencies of events, it uses a rigid, one-size-fits-all time window to force waiting, which can easily lead to two extremes of system-level losses: if the latency parameter is set too high, the underlying memory buffer pool will be suspended for extended periods by invalid long-tail data, triggering a memory overflow risk; if it is set too low, critical late-arriving data packets with strong dependencies will be forcibly truncated and discarded by the underlying system, directly disrupting the consistency of state aggregation calculations.

[0004] Third, when dealing with sudden surges in concurrent operations, conventional system load stabilization strategies often employ delayed, static application-layer blocking and pop-up interception. This passive control cannot predict the transient traffic load at the network layer in advance using calculus and inertia. Furthermore, when system I / O resources approach their limits, the application layer's forced disconnection of data connections often directly triggers socket deadlocks in the underlying business processes, write thread suspensions, and widespread loss of normal data packets. Ultimately, this leads to a system-level crash in the entire heterogeneous data processing cluster, severely lacking the ability to mitigate and recover from degradation at the underlying hardware level. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides an operation management system for hospital data governance, comprising: The composite event data encapsulation module receives business flow data generated by multi-source heterogeneous terminals in real time and encapsulates the business flow data into a composite event-driven data packet; wherein, the composite event-driven data packet contains a built-in metadata structure including the original payload, high-precision timestamp, graph node routing code and event trigger tag; The multimodal semantic alignment module extracts the unstructured text feature vector of the original payload and calculates its similarity with nodes in the standard knowledge graph. When the maximum similarity is lower than the preset matching threshold, a graph-level penalty coefficient is introduced to perform secondary sampling addressing of network-related nodes, and the finally matched standard node identifier is written into the graph node routing code. The dynamic stream processing synchronization module accesses the stream processing engine as an unbounded data stream by taking the composite event-driven data packet containing the routing code of the graph node; it parses the event trigger tag and dynamically adjusts the adaptive water level of the stream processing time window based on the preset business causal chain rules to intercept out-of-order data and perform consistency state aggregation calculation of discrete data packets within the same lifecycle. The load monitoring and permission scheduling module calculates the aggregated resource load in real time and compares it with the preset dynamic load threshold in memory. If the aggregated resource load approaches or exceeds the dynamic load threshold, a control command is generated and sent to the corresponding source terminal through reverse routing to dynamically downgrade the data write permission of the source terminal or move the concurrent request into the delay buffer queue.

[0006] Furthermore, the business flow data is encapsulated into a composite event-driven data packet, including: Identify the network protocol type and terminal identification features of the multi-source heterogeneous terminals; Based on the terminal identification features, a preset distributed state machine model is invoked to extract operation instructions from the business flow data; The operation command is matched with the transition edges of the distributed state machine model to dynamically generate the corresponding event trigger label; wherein, the event trigger label includes a state initialization label, a resource continuous consumption label, and a lifecycle termination label.

[0007] Furthermore, the high-precision timestamp generation mechanism includes: When receiving the business flow data, obtain the physical timestamp of the receiving node; The source clock sequence carried in the business flow data is parsed, and the clock drift compensation value between the source clock sequence and the physical timestamp of the receiving node is calculated. The high-precision timestamp is generated by fusing the physical timestamp, the clock drift compensation value, and the logical vector clock based on the distributed system to ensure the global partial order relationship of concurrent data packets from different heterogeneous terminals in subsequent stream processing.

[0008] Furthermore, the composite event-driven data packet is encapsulated using a header-body separated binary serialization structure; The high-precision timestamp, the map node routing code, and the event trigger tag are encapsulated in a fixed-length message header and allocated consecutive addresses in memory. The original payload is serialized and encapsulated in a variable-length message body; when the stream processing engine parses the event trigger tag, it uses zero-copy technology to read only the fixed-length message header.

[0009] Further, the step of extracting the unstructured text feature vector of the original payload and calculating its similarity with nodes in a standard knowledge graph specifically includes: A pre-trained deep semantic representation model is used to perform entity boundary recognition and word embedding processing on the original payload to generate an input feature vector. ; Extract the standard description text of candidate nodes from the standard knowledge graph and map it to a standard node vector. ; The input feature vector is calculated using the cosine similarity formula. With each standard node vector Initial semantic similarity between The calculation formula is as follows: in, This represents the dot product of two vectors. These represent the magnitudes of the input feature vector and the standard node vector, respectively. The standard node with the highest initial semantic similarity is selected as the anchor node. .

[0010] Furthermore, the method of introducing a graph-level penalty coefficient to perform secondary sampling addressing of network-related nodes is characterized by the following calculation model for the graph-level penalty coefficient: When the anchor node When the initial semantic similarity is lower than the preset matching threshold, secondary sampling based on the graph topology is triggered; Let any associated candidate node within the range of the second sampling be... Dynamically calculate the anchor node To the associated candidate node Graph hierarchy penalty coefficient The formula is as follows: in, An exponential function with base to the natural constant. This indicates that in the standard knowledge graph, from the node To the node The shortest topological path hop count, Representing nodes respectively and nodes Absolute hierarchical depth in the atlas tree structure This represents the absolute value of the difference in hierarchical depth. The path decay factor, For hierarchical crossing penalty factors, and ; After obtaining the graph level penalty coefficient, it is combined with the associated candidate nodes. The initial semantic similarity and graph edge relation weights are used to calculate the associated candidate nodes. Overall alignment score : in, Input feature vector and associated candidate nodes The initial semantic similarity, The penalty coefficient for the map hierarchy is... Represents a node With nodes Preset importance weights for the edge relationships between graphs; The system traverses all associated candidate nodes within the secondary sampling range and selects the comprehensive alignment score. The highest-ranking node is selected as the final matching standard node, and its globally unique node identifier is written into the graph node routing code of the composite event-driven data packet.

[0011] Furthermore, based on preset business causal chain rules, the adaptive water level of the stream processing time window is dynamically adjusted, including: Parse the event trigger tag of the composite event-driven data packet that is currently arriving at the stream processing engine, and use the tag as the starting point to match the corresponding directed acyclic graph topology subgraph in the preset knowledge graph as the current business causal chain. Based on the aforementioned business causal chain, a set of downstream nodes with a strong temporal dependency relationship with the current event is extracted, and a waiting queue for expected events is dynamically generated in memory. Based on the historical average occurrence delay parameters corresponding to each downstream node in the expected event waiting queue, the adaptive water level compensation value of the current stream processing time window is dynamically calculated and updated in real time.

[0012] Furthermore, the mathematical calculation model for the adaptive water level line is as follows: Let the current system time be Adaptive water level for stream processing time window The dynamic calculation formula is: in, As of the current time The highest high-precision timestamp among all observed composite event-driven data packets. The system's preset basic network jitter tolerance constant, This is the queue of expected events currently active in memory. For the first in the queue The average occurrence delay of a desired event in the history recorded in the knowledge graph. For the first A dynamic penalty weighting factor for each expected event, wherein the dynamic penalty weighting factor is positively correlated with the estimated resource consumption load of the corresponding event; Stream processing engine will As a logical clock, the state aggregation calculation is triggered when the logical clock crosses the termination boundary of the time window; It also includes a mandatory computation triggering and memory release mechanism based on causal chain integrity, specifically including: The adaptive water level line Before crossing the termination boundary of the time window, the engine uses a background daemon thread to monitor the matching status of the expected event waiting queue in real time. When it is detected that all composite event-driven data packets corresponding to all expected events in the expected event waiting queue have arrived and have been successfully written into the graph node routing code through the semantic alignment module, an early closure interrupt signal is generated. In response to the early closure interrupt signal, the stream processing engine immediately destroys the current adaptive waterline logic, forces the early closure of the time window and performs consistency state aggregation calculation for cross-terminal concurrent data, while performing garbage collection to release the underlying memory buffer pool allocated to the time window.

[0013] Furthermore, the aggregated resource load is calculated in real time and compared with a preset dynamic load threshold in memory, specifically including: Extract resource consumption parameters from the composite event-driven data packet after the completion status aggregation calculation; An exponential decay model based on a sliding time window is introduced to calculate the current moment in real time. Predicted cumulative resource load The formula is as follows: in, For the first time window within the sliding time window Resource consumption parameters for each processed data packet. For the first The aggregate timestamp of each data packet The time decay constant, This represents the total number of data packets processed within the window. This represents the real-time concurrent arrival rate of composite event-driven data packets monitored by the current network layer. This represents the historical average resource consumption load per package. The preset dynamic load threshold is divided into early warning thresholds. and blocking threshold ,and The predicted resource load cumulative value The system is compared with the warning threshold and the blocking threshold respectively, and the current security state machine identifier of the system is output. The dynamic degradation of the source terminal's data write permissions or the moving of concurrent requests into a delayed buffer queue specifically includes a dynamic data degradation mechanism to handle alert states: When the predicted resource load cumulative value Given the warning threshold With the blocking threshold During this period, a network layer degradation instruction is triggered; The network layer degradation instruction is sent to the source terminal through the reverse routing, forcing the source terminal to automatically strip the unstructured redundant description fields in the original payload and retain only the core coded entities when generating new composite event-driven data packets in the future, so as to compress the packet body size. The stream processing engine allocates a low-priority warm data buffer queue in memory, and redirects subsequent concurrent write requests initiated by the source terminal from the synchronous main thread to the warm data buffer queue for asynchronous queuing processing, thereby reducing the instantaneous I / O throughput pressure of the main database.

[0014] Furthermore, the dynamic degradation of the source terminal's data write permissions or the moving of concurrent requests into a delayed buffer queue also includes a low-level permission forced scheduling mechanism to deal with the blocking state: When the predicted resource load cumulative value Greater than or equal to the blocking threshold At that time, the underlying concurrency isolation command is triggered; The stream processing engine parses the network protocol header of the terminal composite event-driven data packet that causes overload, and extracts the physical media access control address of the source terminal or the socket identifier of a specific process. An asynchronous interrupt signal is sent to the socket identifier through the underlying control channel to directly lock the data writing thread of the corresponding business process of the source terminal; until a key unlocking instruction is received from a high-privilege terminal or the predicted resource load accumulation value falls below the warning threshold, the thread wake-up mechanism is triggered to restore its data writing permission.

[0015] Beneficial effects This invention constructs a hybrid encapsulation alignment architecture that integrates vector clock drift compensation and graph-level cross-validation. This architecture eliminates global partial order disorder caused by asynchronous clocks of distributed nodes at the algorithm execution level. Furthermore, by introducing an exponential decay penalty mechanism based on the topological shortest path and the absolute depth difference, it shields structural interference nodes in the pure text semantic mapping, thus building a data foundation with extremely high purity for the underlying computing engine. This invention introduces a dynamic adaptive water level scheduling model based on a causal chain of a directed acyclic graph and a hardware-level interrupt release mechanism, breaking the bottleneck of invalid memory residency caused by the traditional fixed time window. Under the premise of ensuring strong consistency aggregation of concurrent data across nodes, it achieves instant release of the underlying memory buffer pool and an exponential reduction in garbage collection overhead. By constructing an instantaneous load prediction calculus model that integrates an exponential forgetting mechanism and the integral of concurrent arrival rate, combined with kernel-level warm data buffer redirection and socket-level asynchronous write thread strong lock control, this system achieves millisecond-level precise peak shaving and valley filling of system I / O throughput pressure under extreme concurrent stress testing. It avoids the system deadlock and downtime risks caused by the crude interception of the traditional application layer, and improves the physical resource utilization, computing throughput performance and long-term robustness of heterogeneous computing clusters under high load conditions. Attached Figure Description

[0016] Figure 1 This is a block diagram of the overall system architecture of the present invention; Figure 2 This is a flowchart of the multimodal semantic alignment module of the present invention; Figure 3 This is a flowchart of the dynamic stream processing synchronization module of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] The present invention will now be described in further detail with reference to the accompanying drawings: Example: like Figure 1 As shown, the present invention provides an operation and management system for hospital data governance, comprising: The composite event data encapsulation module receives business flow data generated by multi-source heterogeneous terminals in real time and encapsulates the business flow data into a composite event-driven data packet; wherein, the composite event-driven data packet contains a built-in metadata structure including the original payload, high-precision timestamp, graph node routing code and event trigger tag; The multimodal semantic alignment module extracts the unstructured text feature vector of the original payload and calculates its similarity with nodes in the standard knowledge graph. When the maximum similarity is lower than the preset matching threshold, a graph-level penalty coefficient is introduced to perform secondary sampling addressing of network-related nodes, and the finally matched standard node identifier is written into the graph node routing code. The dynamic stream processing synchronization module accesses the stream processing engine as an unbounded data stream by taking the composite event-driven data packet containing the routing code of the graph node; it parses the event trigger tag and dynamically adjusts the adaptive water level of the stream processing time window based on the preset business causal chain rules to intercept out-of-order data and perform consistency state aggregation calculation of discrete data packets within the same lifecycle. The load monitoring and permission scheduling module calculates the aggregated resource load in real time and compares it with the preset dynamic load threshold in memory. If the aggregated resource load approaches or exceeds the dynamic load threshold, a control command is generated and sent to the corresponding source terminal through reverse routing to dynamically downgrade the data write permission of the source terminal or move the concurrent request into the delay buffer queue.

[0020] Furthermore, the specific implementation process of the composite event data encapsulation module is as follows: When real-time business flow data is received from multiple heterogeneous terminals (such as workstations or background service programs of various business nodes), the composite event data encapsulation module first executes a pre-allocation strategy at the system memory level, and re-encapsulates the business flow data using a header-body separated binary serialization structure to generate a composite event-driven data packet. Those skilled in the art will understand that conventional JSON or XML data formats consume significant CPU resources during deserialization. To overcome this performance bottleneck, the encapsulation mechanism of this application uniformly defines key control information such as high-precision timestamps, graph node routing codes (initially empty), and event trigger tags as a fixed-length message header, and requests the operating system to allocate contiguous addresses for it in memory. Simultaneously, the original unstructured or semi-structured business flow data is used as the original payload and written into a variable-length message body through binary serialization. Based on this specific underlying memory distribution structure, downstream stream processing engines can directly utilize the operating system's zero-copy technology when performing event routing and window calculations. By using memory mapping, they can read and parse only fixed-length message headers, thereby avoiding frequent redundant copying of the massive original payload between kernel mode and user mode and reducing system I / O overhead in high-concurrency scenarios.

[0021] After constructing the basic message structure, to imbue the discrete raw data with business lifecycle semantics, this module does not employ conventional static rule matching. Instead, it introduces a dynamic event tag generation mechanism based on a distributed state machine model. Upon capturing business flow data, the underlying network monitoring daemon of this module first parses the network protocol frames, extracting the physical media access control address or application layer protocol identifier of the source terminal as the terminal identification feature. Subsequently, using the external terminal identification feature as context, the system calls the distributed state machine model residing in memory, extracting operation instructions from the business flow data (such as operation logs or transaction records of a specific business system) through lexical analysis. The system uses the extracted operation instructions as input vectors and performs directed matching with predefined transition edges in the state machine model. For example, when a matching instruction representing the start of a new business process is found, the system generates a "state initialization tag"; when a matching instruction representing the ongoing use of system resources is found, a "resource continuous consumption tag" is generated; and when a matching completion or settlement instruction is found, a "lifecycle termination tag" is generated. The generated event trigger tags are written into the aforementioned message header in real time, turning the originally "dead" data into "live" events that can trigger the state transition of the downstream computing engine.

[0022] Furthermore, considering that multi-source heterogeneous terminals are located in a distributed network topology, problems such as physical clock asynchrony, clock drift, and network transmission congestion and delay are inevitable. If the local time of each terminal is directly used as the timestamp of the data packets, it will cause serious data disorder and logical calculation errors when the downstream stream processing engine performs state aggregation. In order to overcome this underlying problem in a distributed environment, the encapsulation module in this embodiment incorporates a multi-dimensional clock fusion synchronization mechanism. Specifically, at the moment the data stream arrives at the receiving gateway, the system first captures the physical timestamp of the current receiving node through the kernel API; at the same time, it parses the source clock sequence contained in the business flow data and, combined with the recent network round-trip time (RTT) probe data, calculates the clock drift compensation value between the current source clock sequence and the physical timestamp of the receiving node. More importantly, in order to ensure the absolute order of concurrent data, the system also maintains a distributed logical vector clock at the underlying level. The system performs a weighted fusion calculation between the physical time after drift compensation and the current logical vector clock value to generate a high-precision timestamp with global uniqueness and monotonically increasing properties, and then embeds it into the message header.

[0023] This can be understood as follows: This mechanism completely breaks the dependence on a single centralized clock source, and from the perspective of algorithm principle, it ensures that concurrent data packets from different heterogeneous terminals that arrive after experiencing different network delay paths can have a strict and reliable global partial order relationship when they enter the stream processing time window.

[0024] Furthermore, the specific operation process of the multimodal semantic alignment module is as follows: In some preferred embodiments, the aim is to address the semantic gap and topological misalignment issues between unstructured business payloads and standardized knowledge bases. Data generated by multi-source heterogeneous terminals often contains a large amount of non-standardized free text. Directly using rigid string comparison techniques will lead to severe data truncation and mapping loss. To address this, this module constructs a hybrid computational architecture at the underlying level that integrates deep feature extraction and graph structure topology verification, and achieves accurate routing of heterogeneous data through multi-dimensional tensor computation. Simultaneously, the system extracts the standard description text of each candidate node in the standard knowledge graph and maps it to a standard node vector. Based on the multidimensional vector space model, the central processing unit of this module calls the floating-point arithmetic unit to accurately calculate the input feature vector. With each standard node vector Initial semantic similarity between Its underlying calculation calls the cosine similarity mathematical model: in, Represents the inner product of two high-dimensional vectors on the hyperplane. These represent the magnitudes of the input feature vector and the standard node vector in the vector space, respectively. The system sorts the initial semantic similarity sequence generated in the internal memory in descending order, extracts the standard node corresponding to the maximum value, and marks it as the initial anchor node. .

[0025] Given that highly complex knowledge graph systems in specific medical or industrial fields typically exhibit multi-level nested directed acyclic graph (DAG) topologies, relying solely on cosine similarity based on the natural language dimension is highly susceptible to cross-level erroneous mappings. Those skilled in the art should understand that two entity vectors exhibiting high aggregation in the text feature space may belong to completely independent category branches in the underlying actual business graph structure. To overcome this inherent technical deficiency in pure natural language processing, this module sets a strict dynamic matching threshold in the memory register. When the system detects the anchor node... When the initial semantic similarity is lower than the preset threshold, it is determined that there is a risk of structural misalignment in the current input vector. The underlying controller then suspends the current comparison thread and forcibly triggers the secondary sampling addressing mechanism based on the graph topology.

[0026] In the aforementioned secondary sampling addressing mechanism, this application innovatively introduces a graph-level penalty calculation model into the processor instruction set. The system uses the anchor node... Using the origin as the spatial origin, a breadth-first search is performed outwards within a preset topological hop count range to identify a set of candidate network nodes. Let any candidate node within this secondary sampling range be denoted as . The system's underlying algorithm dynamically extracts two core topological space parameters and substitutes them into an exponential decay model to calculate the map hierarchy penalty coefficient. Its physical calculation formula is set as follows: In this mathematical model, An exponential function with base to the natural constant; The representation, from the underlying graph database of the standard knowledge graph, is... To the node The number of hops in the shortest topological path required for traversal; These represent the absolute hierarchical depth of the two nodes in the graph tree structure. That is, the absolute value of the difference in layer depth. The path decay factor, Penalty factor for crossing levels The model successfully transforms the abstract physical differences in graph structures into precise computational constraint variables: the more topological hops a candidate node deviates from the anchor node, or the wider the range of hierarchical levels it traverses, the more non-linearly the attenuation penalty force is increased, thus shielding interference nodes with similar textual semantics but contradictory physical architectures at the algorithm's underlying level.

[0027] After obtaining the graph-level penalty coefficients for each associated candidate node, the system enters the final consistency calibration calculation stage. This module calls the arithmetic logic unit to multiply and fuse the initial semantic similarity, dynamic graph-level penalty coefficients, and predefined topological edge relation weights in the knowledge graph to derive the values ​​for each associated candidate node. Overall alignment score : in, Representation nodes, With nodes The system assigns pre-defined importance weights to the edge relationships within the graph to correct for asymmetry in the local network topology. Based on this, the system performs an optimization traversal, extracting the node corresponding to the highest overall alignment score as the final matching standard node. Finally, the system executes a low-level memory overwrite instruction to precisely write the globally unique identifier of this final matching node into the graph node routing code field of the fixed-length header of the composite event-driven data packet, completing the high-dimensional standardized alignment of the underlying heterogeneous data.

[0028] Specifically, upon receiving a composite event-driven data packet transmitted from the front-end encapsulation module, the parsing thread first extracts the original payload from the variable-length message body. Subsequently, the system calls a pre-trained deep semantic representation model residing in GPU memory to perform entity boundary recognition and high-dimensional word embedding space projection on the original payload, converting it into a fixed-length input feature vector containing contextual features.

[0029] Furthermore, the specific implementation process of the dynamic stream processing synchronization module is as follows: This embodiment aims to address the technical challenge of out-of-order and inconsistent states of concurrent data streams caused by differences in physical transmission link latency in distributed heterogeneous network topologies. As is well known to those skilled in the art, conventional stream processing frameworks often employ a watermark mechanism based on a fixed time delay to intercept out-of-order data. However, in highly complex flow scenarios with strong business causal logic, rigid time windows can easily lead to invalid use of the backend memory buffer pool or the forced truncation and discarding of critical feature data by the system. This module deeply reconstructs the event time progression mechanism at the underlying operator level.

[0030] Specifically, when a composite event-driven data packet containing the routing code of a knowledge graph node is accessed by the stream processing engine as an unbounded data stream, the system's preprocessing operator first parses the event trigger tag in the fixed-length header of the data packet. The stream processing engine does not immediately push the data packet into the computational state machine; instead, it uses the event trigger tag as the addressing starting point and performs local subgraph matching of the directed acyclic graph (DAG) within the knowledge graph database preloaded in the cache. By traversing this DAG topology, the engine's underlying layer accurately extracts the set of downstream nodes with a strong temporal dependency relationship to the currently triggered event, and accordingly dynamically allocates and instantiates a waiting queue for expected events in the memory stack.

[0031] After successfully instantiating the expected event waiting queue, the stream processing engine's core clock scheduler takes over control of the time window boundaries. The system abandons the conventional fixed latency compensation strategy, instead calling the underlying floating-point unit to perform dynamic calculations of the adaptive watermark. Let the current physical time being processed by the system be... Adaptive waterline logic clock for stream processing time window The physical-level computational model is rigorously defined as: in, Represents the current time. The highest precision timestamp carried in all composite event-driven data packets observed by the engine; The basic network jitter tolerance constant is preset for the underlying network layer of the system to absorb random transmission delays from the hardware physical layer; This represents the set of expected events waiting in the current memory.

[0032] It should be noted that for the first [item] in the queue that is in a blocked waiting state... For each expected event, the system dynamically captures its historical average occurrence delay time recorded in the graph. and combine it with the dynamic penalty weight factor Perform a product operation. The weighting factor is mentioned above. The estimated resource consumption load of the event is positively correlated. Through this mathematical model that combines integration and attenuation, the system can dynamically lengthen or shorten the logical clock of the current time window based on the "estimated total delay of the critical causal data packets that have not yet arrived".

[0033] Furthermore, to avoid indefinite suspension and resource exhaustion of the stream processing memory window due to a few long-tailed delayed data, this module, in addition to the adaptive watermark mechanism, introduces a bypass mechanism based on causal chain integrity for forced computation triggering and memory release. This is done within the adaptive logical clock... Throughout its lifecycle, approaching and crossing the time window termination boundary, the engine mounts a background daemon thread in kernel mode. This daemon thread polls and listens non-blockingly for the status of the underlying flag bits of the expected event waiting queue at an extremely high instruction cycle frequency. When the system's underlying Boolean logic gates detect that all composite event-driven data packets corresponding to all expected events in the queue have physically arrived and have been successfully written to the graph node routing code via the front-end module, the system's underlying interrupt controller immediately injects an early closure interrupt signal into the main thread.

[0034] In response to the hardware-level interrupt signal, the main control logic of the stream processing engine directly skips the current adaptive watermark timeout wait instruction, forcibly and prematurely closes the current stream processing time window, and instantly activates the consistency state aggregation calculation operator for cross-terminal concurrent data. More importantly, while the aggregated state is written to the persistent storage medium, the system immediately triggers the underlying garbage collection mechanism, forcibly destroys the current adaptive watermark logic object, and completely releases the underlying memory buffer pool and related thread locks allocated to this specific time window.

[0035] Furthermore, the specific operation process of the load monitoring and access control module is as follows: In this embodiment, the aim is to address the system-level downtime issue caused by the exhaustion of underlying I / O resources or memory overflow when high-concurrency streaming architectures face sudden massive data injections. Conventional threshold control often employs static comparison and rigid blocking strategies, which can easily lead to deadlocks in business threads or large-scale loss of normal data packets.

[0036] Specifically, after the stream processing engine completes the state aggregation calculation of composite event-driven data packets, the background monitoring probe of this module extracts the resource consumption parameters built into each data packet in real time. To achieve accurate prediction of future system load trends, the system does not use conventional linear accumulation calculations, but instead introduces a nonlinear load prediction model based on a sliding time window in the cache. Let the current system time be... This module calls the underlying arithmetic logic unit to calculate the cumulative predicted resource load value at the current moment in real time. Its underlying calculation formula is strictly defined as follows: in, For the first time window within the sliding time window Resource consumption parameters for each processed data packet. Aggregate its timestamps, The time decay constant is set based on memory reclamation efficiency, and is obtained through an exponential decay function. The system can dynamically release the weighted impact of historical load on the current state; the second term on the right side of the equation represents the prediction of future bursts in traffic. This refers to the real-time concurrent arrival rate of composite event-driven data packets monitored by the network layer. To calculate the historical average resource load per packet, the system uses an extremely short time window. The system performs definite integral calculations to accurately deduce transient load increments that are being transmitted in the network link but have not yet reached the computing engine. Based on this high-order continuous mathematical model, the system divides the preset dynamic load safety baseline into warning thresholds. and blocking threshold (and The system then compares the cumulative predicted resource load value with the above-mentioned value using underlying logic to output the current safety state machine identifier of the system.

[0037] When the system state machine indicates that the predicted cumulative resource load value is approaching and falls between the warning threshold and the blocking threshold, the system immediately triggers a dynamic data degradation mechanism to address the warning state. This module does not perform a brute-force full interception; instead, it uses low-level control reverse routing to issue a network layer degradation command to the active source terminal's network interface card. In response to this command, the source terminal's memory-resident program, when generating new composite event-driven data packets, will automatically bypass the conventional full encapsulation logic, forcibly stripping away the massive amounts of unstructured redundant descriptive fields from the original payload, retaining only the minimal core encoded entities, thereby achieving exponential compression of the packet body size at the data source. Simultaneously, the stream processing engine's master node dynamically allocates a low-priority warm data buffer queue in physical memory and, by modifying the kernel-level routing list, redirects subsequent concurrent write requests initiated by the source terminal from the synchronous main thread to the warm data buffer queue for asynchronous queuing. This peak-shaving and valley-filling mechanism alleviates the instantaneous I / O throughput pressure on the main database without interrupting the underlying connection.

[0038] More extremely, when the system's underlying comparator detects that the predicted cumulative resource load has exceeded the blocking threshold... Upon receiving the message, the system will immediately trigger the highest-priority underlying permission forced scheduling mechanism. At this time, the anomaly capture operator of the stream processing engine will deeply analyze the network protocol header of the terminal composite event data packet that caused the overload, accurately extracting the physical media access control address of the source terminal and the socket identifier of its specific business process. Subsequently, the system bypasses the regular interaction of the application layer and directly sends an asynchronous hardware-level interrupt signal to the socket through the underlying control channel, forcibly depriving and locking the data writing thread of the specific business process of the terminal. During the thread lockout, any outgoing data packets will be directly dropped by the operating system's underlying firewall rules until the system receives an RSA asymmetric encryption key unlocking instruction with higher privileges (such as Admin level), or the predicted resource load accumulation value naturally falls back below the warning threshold after exponential decay. Only then will the system's underlying kernel trigger the thread wake-up mechanism, re-release the socket handle, and restore its high-speed write privileges.

[0039] The above 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 with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An operation management system for hospital data governance, characterized by, include: The composite event data encapsulation module receives business flow data generated by multi-source heterogeneous terminals in real time and encapsulates the business flow data into a composite event-driven data packet; wherein, the composite event-driven data packet contains a built-in metadata structure including the original payload, high-precision timestamp, graph node routing code and event trigger tag; The multimodal semantic alignment module extracts the unstructured text feature vector of the original payload and calculates its similarity with nodes in the standard knowledge graph. When the maximum similarity is lower than the preset matching threshold, a graph-level penalty coefficient is introduced to perform secondary sampling addressing of network-related nodes, and the finally matched standard node identifier is written into the graph node routing code. The dynamic stream processing synchronization module accesses the stream processing engine as an unbounded data stream by taking the composite event-driven data packet containing the routing code of the graph node; it parses the event trigger tag and dynamically adjusts the adaptive water level of the stream processing time window based on the preset business causal chain rules to intercept out-of-order data and perform consistency state aggregation calculation of discrete data packets within the same lifecycle. The load monitoring and permission scheduling module calculates the aggregated resource load in real time and compares it with the preset dynamic load threshold in memory. If the aggregated resource load approaches or exceeds the dynamic load threshold, a control command is generated and sent to the corresponding source terminal through reverse routing to dynamically downgrade the data write permission of the source terminal or move the concurrent request into the delay buffer queue.

2. The operational management system for hospital data governance according to claim 1, wherein, The business flow data is encapsulated into a composite event-driven data packet, including: Identify the network protocol type and terminal identification features of the multi-source heterogeneous terminals; Based on the terminal identification features, a preset distributed state machine model is invoked to extract operation instructions from the business flow data; The operation command is matched with the transition edges of the distributed state machine model to dynamically generate the corresponding event trigger label; wherein, the event trigger label includes a state initialization label, a resource continuous consumption label, and a lifecycle termination label.

3. The operational management system for hospital data governance according to claim 2, wherein, The high-precision timestamp generation mechanism includes: When receiving the business flow data, obtain the physical timestamp of the receiving node; The source clock sequence carried in the business flow data is parsed, and the clock drift compensation value between the source clock sequence and the physical timestamp of the receiving node is calculated. The high-precision timestamp is generated by fusing the physical timestamp, the clock drift compensation value, and the logical vector clock based on the distributed system to ensure the global partial order relationship of concurrent data packets from different heterogeneous terminals in subsequent stream processing.

4. The operational management system for hospital data governance according to claim 3, wherein, The composite event-driven data packet is encapsulated using a header-body separated binary serialization structure; The high-precision timestamp, the map node routing code, and the event trigger tag are encapsulated in a fixed-length message header and allocated consecutive addresses in memory. The original payload is serialized and encapsulated in a variable-length message body; when the stream processing engine parses the event trigger tag, it uses zero-copy technology to read only the fixed-length message header.

5. The operational management system for hospital data governance as claimed in claim 4, wherein, The step of extracting the unstructured text feature vector of the original payload and calculating its similarity with nodes in a standard knowledge graph specifically includes: adopting a pre-trained deep semantic representation model to perform entity boundary recognition and word embedding processing on the original load to generate an input feature vector ; Extract the standard description text of candidate nodes from the standard knowledge graph and map it to a standard node vector. ; The input feature vector is calculated using the cosine similarity formula. With each standard node vector Initial semantic similarity between The calculation formula is as follows: in, This represents the dot product of two vectors. These represent the magnitudes of the input feature vector and the standard node vector, respectively. The standard node with the highest initial semantic similarity is selected as the anchor node. .

6. The hospital data governance operation management system according to claim 5, characterized in that, The method of introducing a graph hierarchy penalty coefficient to perform secondary sampling addressing of network-associated nodes is characterized by the following calculation model for the graph hierarchy penalty coefficient: When the anchor node When the initial semantic similarity is lower than the preset matching threshold, secondary sampling based on the graph topology is triggered; Let any associated candidate node within the range of the second sampling be... Dynamically calculate the anchor node To the associated candidate node Graph hierarchy penalty coefficient The formula is as follows: in, An exponential function with base to the natural constant. This indicates that in the standard knowledge graph, from the node To the node The shortest topological path hop count, Representing nodes respectively and nodes Absolute hierarchical depth in the atlas tree structure This represents the absolute value of the difference in hierarchical depth. The path decay factor, For hierarchical crossing penalty factors, and ; After obtaining the graph level penalty coefficient, it is combined with the associated candidate nodes. The initial semantic similarity and graph edge relation weights are used to calculate the associated candidate nodes. Overall alignment score : in, Input feature vector and associated candidate nodes The initial semantic similarity, The penalty coefficient for the aforementioned map hierarchy is... Represents a node With nodes Preset importance weights for the edge relationships between graphs; The system traverses all associated candidate nodes within the secondary sampling range and selects the comprehensive alignment score. The highest-ranking node is selected as the final matching standard node, and its globally unique node identifier is written into the graph node routing code of the composite event-driven data packet.

7. The hospital data governance operation management system according to claim 6, characterized in that, Based on preset business causal chain rules, the adaptive water level of the stream processing time window is dynamically adjusted, including: Parse the event trigger tag of the composite event-driven data packet that is currently arriving at the stream processing engine, and use the tag as the starting point to match the corresponding directed acyclic graph topology subgraph in the preset knowledge graph as the current business causal chain. Based on the aforementioned business causal chain, a set of downstream nodes with a strong temporal dependency relationship with the current event is extracted, and a waiting queue for expected events is dynamically generated in memory. Based on the historical average occurrence delay parameters corresponding to each downstream node in the expected event waiting queue, the adaptive water level compensation value of the current stream processing time window is dynamically calculated and updated in real time.

8. The hospital data governance operation management system according to claim 7, characterized in that, The mathematical calculation model for adaptive water level lines is as follows: Let the current system time be Adaptive water level for stream processing time window The dynamic calculation formula is: in, As of the current time The highest high-precision timestamp among all observed composite event-driven data packets. The system's preset basic network jitter tolerance constant, This is the queue of expected events currently active in memory. For the first in the queue The average occurrence delay of a desired event in the history recorded in the knowledge graph. For the first A dynamic penalty weighting factor for each expected event, wherein the dynamic penalty weighting factor is positively correlated with the estimated resource consumption load of the corresponding event; Stream processing engine will As a logical clock, the state aggregation calculation is triggered when the logical clock crosses the termination boundary of the time window; It also includes a mandatory computation triggering and memory release mechanism based on causal chain integrity, specifically including: The adaptive water level line Before crossing the termination boundary of the time window, the engine uses a background daemon thread to monitor the matching status of the expected event waiting queue in real time. When it is detected that all composite event-driven data packets corresponding to all expected events in the expected event waiting queue have arrived and have been successfully written into the graph node routing code through the semantic alignment module, an early closure interrupt signal is generated. In response to the early closure interrupt signal, the stream processing engine immediately destroys the current adaptive waterline logic, forces the early closure of the time window and performs consistency state aggregation calculation for cross-terminal concurrent data, while performing garbage collection to release the underlying memory buffer pool allocated to the time window.

9. The hospital data governance operation management system according to claim 8, characterized in that, The aggregated resource load is calculated in real time and compared with a preset dynamic load threshold in memory. Specifically, this includes: Extract resource consumption parameters from the composite event-driven data packet after the completion status aggregation calculation; An exponential decay model based on a sliding time window is introduced to calculate the current moment in real time. Predicted cumulative resource load The formula is as follows: in, For the first time within the sliding time window Resource consumption parameters for each processed data packet. For the first The aggregate timestamp of each data packet The time decay constant, This represents the total number of data packets processed within the window. This represents the real-time concurrent arrival rate of composite event-driven data packets monitored by the current network layer. This represents the historical average resource consumption load per package. The preset dynamic load threshold is divided into early warning thresholds. and blocking threshold ,and The predicted resource load cumulative value The system is compared with the warning threshold and the blocking threshold respectively, and the current security state machine identifier of the system is output. The dynamic degradation of the source terminal's data write permissions or the moving of concurrent requests into a delayed buffer queue specifically includes a dynamic data degradation mechanism to handle alert states: When the predicted resource load cumulative value Given the warning threshold With the blocking threshold During this period, a network layer degradation instruction is triggered; The network layer degradation instruction is sent to the source terminal through the reverse routing, forcing the source terminal to automatically strip the unstructured redundant description fields in the original payload and retain only the core coded entities when generating new composite event-driven data packets in the future, so as to compress the packet body size. The stream processing engine allocates a low-priority warm data buffer queue in memory, and redirects subsequent concurrent write requests initiated by the source terminal from the synchronous main thread to the warm data buffer queue for asynchronous queuing processing, thereby reducing the instantaneous I / O throughput pressure of the main database.

10. The hospital data governance operation management system according to claim 9, characterized in that, The dynamic degradation of the source terminal's data write permissions or the moving of concurrent requests into the delay buffer queue also includes a low-level permission forced scheduling mechanism to deal with the blocking state: When the predicted resource load cumulative value Greater than or equal to the blocking threshold At that time, the underlying concurrency isolation command is triggered; The stream processing engine parses the network protocol header of the terminal composite event-driven data packet that causes overload, and extracts the physical media access control address of the source terminal or the socket identifier of a specific process. An asynchronous interrupt signal is sent to the socket identifier through the underlying control channel to directly lock the data writing thread of the corresponding business process of the source terminal; until a key unlocking instruction is received from a high-privilege terminal or the predicted resource load accumulation value falls below the warning threshold, the thread wake-up mechanism is triggered to restore its data writing permission.

Images