Directed acyclic graph configuration logic operation method based on medical information system

Abstract

The invention discloses a directed acyclic graph configuration logic operation method based on a medical information system, and the method comprises the steps: constructing a multi-level medical service directed acyclic graph of a mapping service and a dependency relationship through an analysis engine, providing a visual configuration interface, allowing a user to arrange and define a new medical service API interface logic in a dragging manner, and enabling the user to operate the new medical service API interface logic. Debugging is supported; the customized API logic is analyzed and integrated into a directed acyclic graph to be registered as a new node; a monitoring agent is deployed based on a graph, a node state is calculated, a shared channel is constructed through a message network, when the node state is abnormal, alarm and health assessment are automatically triggered, an optimal node is elected, and system reconfiguration is completed in a coordinated mode. Visual flexible customization of the medical service API and automatic operation and maintenance of the system are achieved, and configurability, reliability and resource efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of directed acyclic graph configuration technology for medical information systems, and more specifically, to a method for operating the configuration logic of a directed acyclic graph in a medical information system. Background Technology

[0002] As healthcare informatization deepens, modern healthcare information systems are becoming increasingly complex. Their core business is often composed of numerous fine-grained microservices or functional modules combined through complex calling relationships. The dependencies between these services exhibit typical networked and hierarchical characteristics. Traditional linear or tree-like service management models struggle to intuitively and accurately describe and manage this complex interaction logic, leading to opaque system architecture, difficulties in service link analysis, and inefficient root cause identification.

[0003] Currently, to quickly respond to ever-changing clinical business needs, systems require high configurability and agile API deployment capabilities. However, existing medical API development and integration methods typically rely on writing code or configuring complex scripts, which not only demands high technical skills from developers but also involves lengthy processes that are difficult for business personnel to directly participate in. Although some visual process orchestration tools exist, most lack deep integration with the underlying service dependency model. Customized new services cannot be automatically integrated into the existing service governance system (such as load monitoring, fault isolation, and elastic scaling), creating "configuration silos." Meanwhile, the system's runtime monitoring and fault recovery mechanisms are often triggered retroactively and manually, lacking predictive health assessment and automated reconfiguration capabilities based on global service topology and real-time status awareness. When some nodes are overloaded or fail, it is difficult to quickly and optimally achieve service routing switching and system reconfiguration, affecting the overall service reliability and resource utilization efficiency.

[0004] Therefore, there is an urgent need for a technical solution that can deeply integrate service dependency visualization modeling, flexible business logic orchestration, and system self-monitoring and self-healing capabilities to address the challenges faced by medical information systems in terms of configurability, automated operation and maintenance, and reliability. Summary of the Invention

[0005] The purpose of this invention is to provide a method for configuring logic operation of a directed acyclic graph based on a medical information system, so as to solve the above-mentioned problems existing in the prior art.

[0006] The application is as follows: A method for configuring logic operation of a directed acyclic graph based on a medical information system includes: S100: The medical configuration parsing engine reads the information of various services registered in the medical service registry, extracts the access paths of each service and their mutual call dependencies, maps each independent service access path to a directed acyclic graph node, maps the call relationship between services to weighted directed edges connecting the corresponding nodes, and initializes the load value according to the historical load data of the nodes, thereby constructing a multi-level medical service dependency directed acyclic graph that reflects the existing service topology and dependency relationship of the system, as a unified basic model for system visualization orchestration and runtime management. S200. Based on the node and connection paradigm defined by the multi-level medical service dependency directed acyclic graph, a visual custom configuration interface is provided to users to enable business orchestration capabilities. This configuration interface allows users to define and orchestrate new medical business API interfaces on top of the basic model by dragging and dropping nodes and connections. Specifically, the following operations are performed: S201. Receive basic configuration information of the newly created medical business API interface input by the user. The information includes the globally unique identifier of the API interface, the business type and function description. S202. In response to the user's new creation command, a pre-built business logic node library compatible with the node type of the directed acyclic graph is presented in the visual logic canvas. The node library includes data operation nodes, business processing nodes and dedicated nodes for connecting the start and end points of API logic. S203. In response to the user's drag-and-drop operation from the node library to the canvas, instantiate the selected node type and place it in the specified coordinate area of ​​the canvas as a logical component of the API interface to be orchestrated. S204. In response to the user's click operation on the node instance in the canvas, dynamically present a visual parameter configuration form that matches the node type, and receive and save the execution parameters and configuration information of the user-defined node behavior. S205. In response to the user's connection operation between node instances, the start node, one or more business logic nodes and the end node are connected in the order of business execution by dragging the connection line to form a business logic link with a clear unidirectional execution flow and data dependency. This link completely defines the internal execution logic of the medical business API interface. S206. In response to the user's debugging command, start simulation debugging based on the business logic link to verify the logical validity and execution accuracy of the medical business API interface; S300: The medical business API interface logic definition generated through visualization customization in step S200, which conforms to the directed acyclic graph method, is passed to the medical configuration parsing engine for parsing. The medical configuration parsing engine maps the medical business API interface logic link to a composite node and its internal child nodes and directed edge structure in the multi-level medical service dependency directed acyclic graph, calculates its weight and load expectation, and finally registers and updates it as a new discoverable and callable service node to the medical service registry center and the basic service dependency graph model, completing the seamless integration from visualization orchestration to system service. S400. Deploy a medical monitoring agent program based on the multi-level medical service dependency directed acyclic graph, collect system operation data and calculate the medical node operation status evaluation value, and build a mesh sharing channel through the medical message distribution network. S500. When the evaluation value of the running status of the medical node is lower than the preset threshold, an alarm, health assessment and node election process are automatically triggered. The node with the best health status is selected as the medical execution node based on the consensus algorithm, and the distributed transaction coordinator is called to complete the reconfiguration of the medical system based on the directed acyclic graph.

[0007] Furthermore, S300 specifically includes: The start node, end node, and various business logic nodes defined in the business logic chain are mapped to the newly added API interface definition nodes in the multi-level medical service dependency directed acyclic graph. The connection lines between nodes are mapped to directed edges in the directed acyclic graph that connect the nodes defined by the API interface, so as to represent the logical call order and data flow direction within the interface. Based on the node execution parameters and configuration information, execution weights are assigned to the corresponding directed edges, and a standardized call path description and parameter binding template for the API interface are generated. The API interface definitions that have completed mapping, weighting, and template generation are integrated into the logical structure of the multi-level medical service dependency directed acyclic graph, and the access endpoint of the API interface is registered with the medical service registry center to complete the customized creation and system-level release of the new business API.

[0008] Furthermore, after step S300, the method further includes: The connectivity and load distribution of nodes in the integrated multi-level medical service dependency directed acyclic graph are analyzed using the medical configuration parsing engine to establish a medical service node correlation matrix. Calculate the dependency depth and association strength of the medical business API interface and its internal components in the directed acyclic graph; Based on dependency depth, association strength, and the current system load, a medical configuration parameter execution sequence that takes into account resource configuration optimization is dynamically generated to guide the efficient scheduling and execution of the API interface.

[0009] Furthermore, the step of dynamically generating the medical configuration parameter execution sequence that considers resource allocation optimization based on dependency depth, association strength, and the current system load status includes: The dependency depth and association strength of each configuration parameter within the medical business API interface are standardized in the directed acyclic graph. The weight coefficients of dependency depth and correlation strength are dynamically adjusted based on the real-time load status of the medical system, and the adjusted values ​​are linearly combined to obtain the execution priority of each configuration parameter. The parameters are sorted in descending order according to the execution priority, and parameter pairs with transaction dependencies are identified and their sequences are ensured to be adjacent. The system detects the real-time load of nodes, and when an overloaded node is detected, it shifts the execution order of the configuration parameters it carries to the next node. The configuration parameters deployed on the same physical node are clustered, and the sequence is adjusted while ensuring the dependencies, ultimately generating an execution sequence that satisfies the resource configuration optimization constraints of the directed acyclic graph.

[0010] Furthermore, after registering the access endpoint of the API interface with the healthcare service registry in step S300, the process also includes: Read the registered medical business API interface definitions and parse their business logic links; Based on the medical service node correlation matrix and the current system load, the node configuration parameters and execution paths involved in the API interface are optimized and grouped. An API interface execution template is constructed for the optimized parameter set, and a medical transaction unit that ensures the atomicity and consistency of the interface is created through the distributed transaction coordinator. Generate and execute a sequence of medical configuration execution instructions containing the complete logic of the medical business API interface to complete the business deployment of the new customized API or the business update of the existing API.

[0011] Furthermore, step S400 specifically includes: The medical monitoring agent program determines the current medical load level based on the load value, calculates the dynamic sampling coefficient based on the exponential backoff algorithm, and sets the adaptive medical data sampling period accordingly. According to the sampling period, the operating status data of the medical system is collected periodically, and the operating status evaluation value of the medical node is obtained after normalization and weighted calculation. Construct a medical system status message containing status assessment values ​​and node information, exchange information with neighboring nodes through a medical message distribution network, and establish a medical message synchronization mechanism; Based on the aforementioned synchronization mechanism, a medical priority message routing table is constructed to control the message forwarding scope and path, forming a mesh shared channel with resource optimization and fault tolerance capabilities.

[0012] Furthermore, step S500 specifically includes: When the medical node's operational status assessment value is lower than a preset threshold, the medical alarm processing engine broadcasts a medical alarm message through the mesh sharing channel. Receive local status assessment values ​​from neighboring nodes to confirm alarms and generate a health status assessment report for medical nodes; Based on the health status assessment report, a consensus algorithm is used to elect the node with the best health status as the medical execution node; The medical alarm processing engine triggers the distributed transaction coordinator to execute the system reconfiguration process based on the election results and the current load status, and updates the medical service registry and the mesh sharing channel.

[0013] Furthermore, the method also includes: Establish an API interface access and configuration permission control list bound to the medical role, and manage permissions for operations performed through the visual custom configuration interface; Implement audit logging of API interface configuration changes, track all editing operation history in the visual canvas, and provide version control and rollback functions for API interface logic links; The business logic link definition, node configuration parameters, and execution instruction sequence of the API interface are encrypted, stored, and transmitted. The business logic simulation debugging engine simulates API logic chain calls based on test cases in an isolated sandbox, generating debugging analysis reports to verify the accuracy of the interface.

[0014] Compared with the prior art, the present invention achieves the following beneficial effects: This invention constructs a multi-level directed acyclic graph (DAG) of medical services and their dependencies using a parsing engine. It provides a visual configuration interface, allowing users to drag and drop to define new medical business API interface logic and supports debugging. Customized API logic is parsed and integrated into the DAG and registered as new nodes. A monitoring agent is deployed based on the graph, calculating node status and building a shared channel through a message network. When a node's status is abnormal, alarms and health assessments are automatically triggered, an optimal node is elected, and system reconfiguration is coordinated. This invention achieves visualized and flexible customization of medical business APIs and automated system operation and maintenance, improving configurability, reliability, and resource efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a directed acyclic graph configuration logic operation method based on a medical information system, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the business logic of a medical system based on a directed acyclic graph configuration logic operation method for a medical information system, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram of the API development interface for a directed acyclic graph configuration logic operation method based on a medical information system, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of API development and debugging for a directed acyclic graph configuration logic operation method based on a medical information system, provided by an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings.

[0017] Example 1

[0018] This invention provides a method for configuring logic operation based on a directed acyclic graph in a medical information system, such as... Figure 1 This includes the following steps: S100: The medical configuration parsing engine reads the information of various services registered in the medical service registry, extracts the access paths of each service and their mutual call dependencies, maps each independent service access path to a directed acyclic graph node, maps the call relationship between services to weighted directed edges connecting the corresponding nodes, and initializes the load value according to the historical load data of the nodes, thereby constructing a multi-level medical service dependency directed acyclic graph that reflects the existing service topology and dependency relationship of the system, as a unified basic model for system visualization orchestration and runtime management. S200. Based on the node and connection paradigm defined by the multi-level medical service dependency directed acyclic graph, a visual custom configuration interface is provided to users to enable business orchestration capabilities. This configuration interface allows users to define and orchestrate new medical business API interfaces on top of the basic model by dragging and dropping nodes and connections. Specifically, the following operations are performed: S201. Receive basic configuration information of the newly created medical business API interface input by the user. The information includes the globally unique identifier of the API interface, the business type and function description. S202. In response to the user's new creation command, a pre-built business logic node library compatible with the node type of the directed acyclic graph is presented in the visual logic canvas. The node library includes data operation nodes, business processing nodes and dedicated nodes for connecting the start and end points of API logic. S203. In response to the user's drag-and-drop operation from the node library to the canvas, instantiate the selected node type and place it in the specified coordinate area of ​​the canvas as a logical component of the API interface to be orchestrated. S204. In response to the user's click operation on the node instance in the canvas, dynamically present a visual parameter configuration form that matches the node type, and receive and save the execution parameters and configuration information of the user-defined node behavior. S205. In response to the user's connection operation between node instances, the start node, one or more business logic nodes and the end node are connected in the order of business execution by dragging the connection line to form a business logic link with a clear unidirectional execution flow and data dependency. This link completely defines the internal execution logic of the medical business API interface. S206. In response to the user's debugging command, start simulation debugging based on the business logic link to verify the logical validity and execution accuracy of the medical business API interface; It should be noted that the above steps allow users to customize the backend business logic process, such as... Figure 2 ,like Figure 2 He Ru Figure 4 As shown: 1. User fills in basic business information to create new logic; 2. After completing the logic creation, you can drag the required nodes from the preset business logic node library on the left (such as database add, delete, modify, query, etc. nodes) to the canvas area in the visual logic canvas; 3. Click on a node in the canvas to fine-tune the node parameters and configuration information through a visual form; 4. After the nodes are configured, users can drag and drop connection lines to connect each business node sequentially from the starting node to the ending node according to the business logic, forming a complete business logic link. 5. After the process is set up, users can click the debug button to start business logic simulation debugging and verify the effectiveness and accuracy of the entire business logic triggering and calling process.

[0019] S300: The medical business API interface logic definition generated through visualization customization in step S200, which conforms to the directed acyclic graph method, is passed to the medical configuration parsing engine for parsing. The medical configuration parsing engine maps the medical business API interface logic link to a composite node and its internal child nodes and directed edge structure in the multi-level medical service dependency directed acyclic graph, calculates its weight and load expectation, and finally registers and updates it as a new discoverable and callable service node to the medical service registry center and the basic service dependency graph model, completing the seamless integration from visualization orchestration to system service. S400. Deploy a medical monitoring agent program based on the multi-level medical service dependency directed acyclic graph, collect system operation data and calculate the medical node operation status evaluation value, and build a mesh sharing channel through the medical message distribution network. S500. When the evaluation value of the running status of the medical node is lower than the preset threshold, an alarm, health assessment and node election process are automatically triggered. The node with the best health status is selected as the medical execution node based on the consensus algorithm, and the distributed transaction coordinator is called to complete the reconfiguration of the medical system based on the directed acyclic graph.

[0020] Specifically, this embodiment constructs a multi-level directed acyclic graph (DAG) of medical services and their dependencies through a parsing engine. It provides a visual configuration interface, allowing users to drag and drop to define new medical business API interface logic and supports debugging. Customized API logic is parsed and integrated into the DAG and registered as new nodes. A monitoring agent is deployed based on the graph, calculates node status, and builds a shared channel through a message network. When a node's status is abnormal, alarms and health assessments are automatically triggered, the optimal node is elected, and system reconfiguration is coordinated. This embodiment achieves visualized and flexible customization of medical business APIs and automated system operation and maintenance, improving configurability, reliability, and resource efficiency.

[0021] In the above embodiments, specifically, S300 includes: The start node, end node, and various business logic nodes defined in the business logic chain are mapped to the newly added API interface definition nodes in the multi-level medical service dependency directed acyclic graph. The connection lines between nodes are mapped to directed edges in the directed acyclic graph that connect the nodes defined by the API interface, so as to represent the logical call order and data flow direction within the interface. Based on the node execution parameters and configuration information, execution weights are assigned to the corresponding directed edges, and a standardized call path description and parameter binding template for the API interface are generated. The API interface definitions that have completed mapping, weighting, and template generation are integrated into the logical structure of the multi-level medical service dependency directed acyclic graph, and the access endpoint of the API interface is registered with the medical service registry center to complete the customized creation and system-level release of the new business API.

[0022] In the above embodiment, specifically, after step S300, the method further includes: The connectivity and load distribution of nodes in the integrated multi-level medical service dependency directed acyclic graph are analyzed using the medical configuration parsing engine to establish a medical service node correlation matrix. Calculate the dependency depth and association strength of the medical business API interface and its internal components in the directed acyclic graph; Based on dependency depth, association strength, and the current system load, a medical configuration parameter execution sequence that takes into account resource configuration optimization is dynamically generated to guide the efficient scheduling and execution of the API interface.

[0023] In the above embodiments, specifically, the step of dynamically generating the medical configuration parameter execution sequence that considers resource allocation optimization based on dependency depth, association strength, and the current system load status includes: The dependency depth and association strength of each configuration parameter within the medical business API interface are standardized in the directed acyclic graph. The weight coefficients of dependency depth and correlation strength are dynamically adjusted based on the real-time load status of the medical system, and the adjusted values ​​are linearly combined to obtain the execution priority of each configuration parameter. The parameters are sorted in descending order according to the execution priority, and parameter pairs with transaction dependencies are identified and their sequences are ensured to be adjacent. The system detects the real-time load of nodes, and when an overloaded node is detected, it shifts the execution order of the configuration parameters it carries to the next node. The configuration parameters deployed on the same physical node are clustered, and the sequence is adjusted while ensuring the dependencies, ultimately generating an execution sequence that satisfies the resource configuration optimization constraints of the directed acyclic graph.

[0024] In the above embodiment, specifically, after registering the access endpoint of the API interface with the medical service registry in step S300, the method further includes: Read the registered medical business API interface definitions and parse their business logic links; Based on the medical service node correlation matrix and the current system load, the node configuration parameters and execution paths involved in the API interface are optimized and grouped. An API interface execution template is constructed for the optimized parameter set, and a medical transaction unit that ensures the atomicity and consistency of the interface is created through the distributed transaction coordinator. Generate and execute a sequence of medical configuration execution instructions containing the complete logic of the medical business API interface to complete the business deployment of the new customized API or the business update of the existing API.

[0025] In the above embodiments, step S400 specifically includes: The medical monitoring agent program determines the current medical load level based on the load value, calculates the dynamic sampling coefficient based on the exponential backoff algorithm, and sets the adaptive medical data sampling period accordingly. According to the sampling period, the operating status data of the medical system is collected periodically, and the operating status evaluation value of the medical node is obtained after normalization and weighted calculation. Construct a medical system status message containing status assessment values ​​and node information, exchange information with neighboring nodes through a medical message distribution network, and establish a medical message synchronization mechanism; Based on the aforementioned synchronization mechanism, a medical priority message routing table is constructed to control the message forwarding scope and path, forming a mesh shared channel with resource optimization and fault tolerance capabilities.

[0026] In the above embodiments, step S500 specifically includes: When the medical node's operational status assessment value is lower than a preset threshold, the medical alarm processing engine broadcasts a medical alarm message through the mesh sharing channel. Receive local status assessment values ​​from neighboring nodes to confirm alarms and generate a health status assessment report for medical nodes; Based on the health status assessment report, a consensus algorithm is used to elect the node with the best health status as the medical execution node; The medical alarm processing engine triggers the distributed transaction coordinator to execute the system reconfiguration process based on the election results and the current load status, and updates the medical service registry and the mesh sharing channel.

[0027] In the above embodiments, specifically, the method further includes: Establish an API interface access and configuration permission control list bound to the medical role, and manage permissions for operations performed through the visual custom configuration interface; Implement audit logging of API interface configuration changes, track all editing operation history in the visual canvas, and provide version control and rollback functions for API interface logic links; The business logic link definition, node configuration parameters, and execution instruction sequence of the API interface are encrypted, stored, and transmitted. The business logic simulation debugging engine simulates API logic chain calls based on test cases in an isolated sandbox, generating debugging analysis reports to verify the accuracy of the interface.

[0028] It should be understood that the above embodiments are one or more embodiments of the present invention, and there are many other embodiments and variations based on the present invention; any variations and modifications made by those skilled in the art through the present invention without making pioneering innovations are all within the protection scope of the present invention.

Claims

1. A method for configuring logic operation of a directed acyclic graph based on a medical information system, characterized in that, Includes the following steps: S100: The medical configuration parsing engine reads the information of various services registered in the medical service registry, extracts the access paths of each service and their mutual call dependencies, maps each independent service access path to a directed acyclic graph node, maps the call relationship between services to weighted directed edges connecting the corresponding nodes, and initializes the load value according to the historical load data of the nodes, thereby constructing a multi-level medical service dependency directed acyclic graph that reflects the existing service topology and dependency relationship of the system, as a unified basic model for system visualization orchestration and runtime management. S200. Based on the node and connection paradigm defined by the multi-level medical service dependency directed acyclic graph, a visual custom configuration interface is provided to users to enable business orchestration capabilities. This configuration interface allows users to define and orchestrate new medical business API interfaces on top of the basic model by dragging and dropping nodes and connections. Specifically, the following operations are performed: S201. Receive basic configuration information of the newly created medical business API interface input by the user. The information includes the globally unique identifier of the API interface, the business type and function description. S202. In response to the user's new creation command, a pre-built business logic node library compatible with the node type of the directed acyclic graph is presented in the visual logic canvas. The node library includes data operation nodes, business processing nodes and dedicated nodes for connecting the start and end points of API logic. S203. In response to the user's drag-and-drop operation from the node library to the canvas, instantiate the selected node type and place it in the specified coordinate area of ​​the canvas as a logical component of the API interface to be orchestrated. S204. In response to the user's click operation on the node instance in the canvas, dynamically present a visual parameter configuration form that matches the node type, and receive and save the execution parameters and configuration information of the user-defined node behavior. S205. In response to the user's connection operation between node instances, the start node, one or more business logic nodes and the end node are connected in the order of business execution by dragging the connection line to form a business logic link with a clear unidirectional execution flow and data dependency. This link completely defines the internal execution logic of the medical business API interface. S206. In response to the user's debugging command, start simulation debugging based on the business logic link to verify the logical validity and execution accuracy of the medical business API interface; S300: The medical business API interface logic definition generated through visualization customization in step S200, which conforms to the directed acyclic graph method, is passed to the medical configuration parsing engine for parsing. The medical configuration parsing engine maps the medical business API interface logic link to a composite node and its internal child nodes and directed edge structure in the multi-level medical service dependency directed acyclic graph, calculates its weight and load expectation, and finally registers and updates it as a new discoverable and callable service node to the medical service registry center and the basic service dependency graph model, completing the seamless integration from visualization orchestration to system service. S400. Deploy a medical monitoring agent program based on the multi-level medical service dependency directed acyclic graph, collect system operation data and calculate the medical node operation status evaluation value, and build a mesh sharing channel through the medical message distribution network. S500. When the evaluation value of the running status of the medical node is lower than the preset threshold, an alarm, health assessment and node election process are automatically triggered. The node with the best health status is selected as the medical execution node based on the consensus algorithm, and the distributed transaction coordinator is called to complete the reconfiguration of the medical system based on the directed acyclic graph.

2. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 1, characterized in that, Specifically, S300 includes: The start node, end node, and various business logic nodes defined in the business logic chain are mapped to the newly added API interface definition nodes in the multi-level medical service dependency directed acyclic graph. The connection lines between nodes are mapped to directed edges in the directed acyclic graph that connect the nodes defined by the API interface, so as to represent the logical call order and data flow direction within the interface. Based on the node execution parameters and configuration information, execution weights are assigned to the corresponding directed edges, and a standardized call path description and parameter binding template for the API interface are generated. The API interface definitions that have completed mapping, weighting, and template generation are integrated into the logical structure of the multi-level medical service dependency directed acyclic graph, and the access endpoint of the API interface is registered with the medical service registry center to complete the customized creation and system-level release of the new business API.

3. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 2, characterized in that, Following step S300, the method further includes: The connectivity and load distribution of nodes in the integrated multi-level medical service dependency directed acyclic graph are analyzed using the medical configuration parsing engine to establish a medical service node correlation matrix. Calculate the dependency depth and association strength of the medical business API interface and its internal components in the directed acyclic graph; Based on dependency depth, association strength, and the current system load, a medical configuration parameter execution sequence that takes into account resource configuration optimization is dynamically generated to guide the efficient scheduling and execution of the API interface.

4. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 3, characterized in that, The process of dynamically generating a medical configuration parameter execution sequence that considers resource allocation optimization based on dependency depth, association strength, and the current system load includes: The dependency depth and association strength of each configuration parameter within the medical business API interface are standardized in the directed acyclic graph. The weight coefficients of dependency depth and correlation strength are dynamically adjusted based on the real-time load status of the medical system, and the adjusted values ​​are linearly combined to obtain the execution priority of each configuration parameter. The parameters are sorted in descending order according to the execution priority, and parameter pairs with transaction dependencies are identified and their sequences are ensured to be adjacent. The system detects the real-time load of nodes, and when an overloaded node is detected, it shifts the execution order of the configuration parameters it carries to the next node. The configuration parameters deployed on the same physical node are clustered, and the sequence is adjusted while ensuring the dependencies, ultimately generating an execution sequence that satisfies the resource configuration optimization constraints of the directed acyclic graph.

5. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 2, characterized in that, After registering the access endpoint of the API interface with the healthcare service registry in step S300, the process further includes: Read the registered medical business API interface definitions and parse their business logic links; Based on the medical service node correlation matrix and the current system load, the node configuration parameters and execution paths involved in the API interface are optimized and grouped. An API interface execution template is constructed for the optimized parameter set, and a medical transaction unit that ensures the atomicity and consistency of the interface is created through the distributed transaction coordinator. Generate and execute a sequence of medical configuration execution instructions containing the complete logic of the medical business API interface to complete the business deployment of the new customized API or the business update of the existing API.

6. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 1, characterized in that, Step S400 specifically includes: The medical monitoring agent program determines the current medical load level based on the load value, calculates the dynamic sampling coefficient based on the exponential backoff algorithm, and sets the adaptive medical data sampling period accordingly. According to the sampling period, the operating status data of the medical system is collected periodically, and the operating status evaluation value of the medical node is obtained after normalization and weighted calculation. Construct a medical system status message containing status assessment values ​​and node information, exchange information with neighboring nodes through a medical message distribution network, and establish a medical message synchronization mechanism; Based on the aforementioned synchronization mechanism, a medical priority message routing table is constructed to control the message forwarding scope and path, forming a mesh shared channel with resource optimization and fault tolerance capabilities.

7. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 1, characterized in that, Step S500 specifically includes: When the medical node's operational status assessment value is lower than a preset threshold, the medical alarm processing engine broadcasts a medical alarm message through the mesh sharing channel. Receive local status assessment values ​​from neighboring nodes to confirm alarms and generate a health status assessment report for medical nodes; Based on the health status assessment report, a consensus algorithm is used to elect the node with the best health status as the medical execution node; The medical alarm processing engine triggers the distributed transaction coordinator to execute the system reconfiguration process based on the election results and the current load status, and updates the medical service registry and the mesh sharing channel.

8. The method for configuring logic operation of a directed acyclic graph based on a medical information system according to claim 1, characterized in that, The method further includes: Establish an API interface access and configuration permission control list bound to the medical role, and manage permissions for operations performed through the visual custom configuration interface; Implement audit logging of API interface configuration changes, track all editing operation history in the visual canvas, and provide version control and rollback functions for API interface logic links; The business logic link definition, node configuration parameters, and execution instruction sequence of the API interface are encrypted, stored, and transmitted. The business logic simulation debugging engine simulates API logic chain calls based on test cases in an isolated sandbox, generating debugging analysis reports to verify the accuracy of the interface.

Images