Obstetric emergency department operation process simulation optimization method based on digital twinning technology

Abstract

The application relates to the field of medical informatization, and discloses an obstetric emergency department operation process simulation optimization method based on digital twin technology. The method first acquires physiological parameters of a lying-in woman and a real-time occupation state of resources, and quantizes physiological abnormal fluctuation into a resource occupation probability vector by using a mapping operator. When the prediction probability reaches a trigger threshold, the system starts a speculative branch process in a digital twin space, and performs atomic locking of cross-physical-domain resources through a two-phase commit protocol. For a multi-concurrent scene, the system uses a conflict entropy model to perform global path simulation search and process dynamic reconstruction to balance the operation load of the department. Finally, a consistency checking module compares the deviation between physical instructions and speculative instructions, and realizes resource state conversion or triggers a self-healing rollback protocol. The application solves the problems of disconnection between obstetric emergency monitoring data and business processes, resource scheduling lag, and difficulty in suppressing concurrent conflicts.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, specifically a simulation and optimization method for the operational process of an obstetric emergency department based on digital twin technology. Background Technology

[0002] Obstetric emergencies are characterized by their extreme urgency and timeliness; changes in the physiological states of both mother and infant directly dictate adjustments to clinical pathways. However, current hospital management systems and process simulation methods still have technical limitations in handling such highly dynamic scenarios as obstetric emergencies.

[0003] Existing process optimizations are mostly based on static queuing theory or post-event business data analysis, with clinical physiological monitoring data and departmental resource scheduling systems remaining isolated from each other. Process initiation typically relies on explicit medical orders or instructions from healthcare professionals. This instruction-driven model logically lags behind the occurrence of physiological risks, failing to provide proactive responses to emergency situations and resulting in unnecessary waiting times for mothers during transport and surgical preparation.

[0004] Meanwhile, obstetric emergencies involve cross-departmental collaboration across multiple physical domains, including operating rooms, blood banks, and transport systems. Existing IT solutions lack the technical capability for atomized management of these multi-dimensional resources; resource nodes are typically in a discrete scheduling state, making it difficult to ensure all relevant resources are ready synchronously at the same point in time. This lack of coordination often results in situations where, even after one resource is in place, other supporting resources are still waiting, reducing the overall continuity of emergency response.

[0005] Furthermore, when a department faces a concurrent scenario with multiple high-risk pregnant women being admitted at the same time, existing technologies lack an effective logical model to assess the degree of conflict and operational disorder of resources across the entire system. Due to the lack of a global optimization algorithm that dynamically adjusts the allocation sequence of tasks with different priorities, limited medical resources often experience excessive competition or uneven distribution in certain areas, making it difficult to maintain the overall workload balance of the department under high-load operation. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a simulation optimization method for the operational process of obstetric emergency departments based on digital twin technology. This method solves the problems of delayed resource scheduling response caused by the decoupling of maternal physiological monitoring data from clinical business processes, poor physical execution coordination caused by the discrete scheduling of multi-dimensional resources across departments, and resource allocation imbalance caused by the lack of a global conflict evaluation model in multi-concurrent emergency scenarios.

[0007] To achieve the above objectives, this invention provides the following technical solution: a simulation and optimization method for the operational flow of an obstetric emergency department based on digital twin technology. This method achieves dynamic reconstruction of obstetric emergency resources through physiological feature-driven pre-scheduling of business processes. Specifically, it includes the following steps: The system collects a set of maternal physiological parameters transmitted through the physical sensing layer. This set of physiological parameters includes fetal heart rate, uterine contraction pressure, mean arterial pressure, and blood oxygen saturation. Simultaneously, the system extracts a resource status matrix that records the real-time occupancy status of departmental resources.

[0008] The system executes physiological-process load vector mapping logic. The physiological load mapping module calls preset mapping operators to process the set of physiological parameters and generate a resource occupancy probability vector. This vector is used to reflect the potential demand probability of parturients for specific emergency resources such as emergency operating rooms, blood banks, and neonatal resuscitation units. The mapping process quantifies the deviation of current physiological characteristics from preset physiological baselines, realizing the correlation and transformation of physiological anomalies into business process requirements.

[0009] When the resource occupancy probability meets the triggering conditions, the system executes speculative branch triggering and atomic latching. When the predicted resource demand probability exceeds the judgment limit determined by the dynamic threshold and risk correction factor, the speculative execution module generates a speculative branch process within the digital twin space. The atomic transaction control module initiates a two-phase commit protocol: in the pre-lock request phase, it sends a query command to the controller of the relevant physical node and obtains the availability verification result; in the latch execution phase, if the global consistency condition is met, it performs logical locking on the target resource in the physical device control logic. This step aims to pre-occupy necessary medical resources before physical commands are generated.

[0010] To address resource contention scenarios where multiple pregnant women concurrently trigger presumptive execution, the system performs global simulation optimization based on conflict entropy. The conflict entropy optimization module combines risk weights determined by triage levels with resource scarcity factors to calculate the timeliness weight coefficients of each process and constructs a global conflict entropy model to quantify the degree of disorder in global resource contention. The system traverses scheduling sequences within the simulation space to find the optimal set of paths that minimizes the global conflict entropy and issues dynamic process reconfiguration commands to balance the overall operational load of the department.

[0011] The system performs consistency checks and self-healing rollbacks. The consistency check module captures the actual medical instructions in the physical space at key process nodes and compares them with the inferred instructions in the digital twin space. The system calculates the deviation factor between the actual and inferred instructions to determine the consistency of the prediction. If an instruction deviation is detected, the system immediately triggers a rollback function, performs an atomic undo operation, and releases the pre-locked physical resource capacity.

[0012] This invention provides a method for simulating and optimizing the operational flow of an obstetric emergency department based on digital twin technology. It has the following beneficial effects: 1. This invention transforms real-time collected maternal physiological parameters into resource occupancy probabilities through a physiological load mapping module, and activates a pre-simulation branch in conjunction with a speculative execution module, thereby realizing the transformation of medical resource allocation from instruction-driven to trend-driven, intervening in the allocation process of physical resources in advance, and shortening the resource waiting time caused by manual decision-making and information flow in the obstetric emergency process.

[0013] 2. This invention utilizes an atomic transaction control module to execute a two-phase commit protocol for cross-physical domain resources, ensuring that resources distributed across different physical nodes, such as operating rooms, blood banks, and transport elevators, possess atomicity during the speculative execution phase. This guarantees the consistency of synchronous locking or release of multiple types of resources, avoids interruptions in the emergency response process due to the absence of some resources, and improves the reliability of multi-department collaborative scheduling.

[0014] 3. This invention introduces a conflict entropy optimization module to quantify the degree of competition and disorder of resources across the entire department. By simulating the evolution and path search of multi-concurrent speculative branch processes, it achieves dynamic balancing and process reconstruction of limited medical resources under high-load conditions. It can automatically optimize the timing of resource allocation based on timeliness weights, ensuring the scientific distribution of the overall operational load of the department. Attached Figure Description

[0015] Figure 1 This is a structural diagram of the present invention; Figure 2 This is a flowchart of the present invention.

[0016] The system consists of: 10. Physical perception layer; 20. Data transmission layer; 30. Digital twin engine layer; 31. Physiological load mapping module; 32. Speculative execution module; 33. Atomic transaction control module; 34. Conflict entropy optimization module; 35. Consistency verification module; and 40. Application feedback layer. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example: Please see the appendix Figure 1 The present invention provides a simulation and optimization system for the operation process of an obstetric emergency department based on digital twin technology, including a physical perception layer 10, a data transmission layer 20, a digital twin engine layer 30, and an application feedback layer 40.

[0019] The physical sensing layer 10 is configured within the physical space of the obstetric emergency department. The physical sensing layer 10 includes a wearable medical monitoring terminal deployed on the mother and IoT sensing nodes deployed in wards, operating rooms, and blood banks. The physical sensing layer 10 is used to collect real-time multidimensional physiological parameters of the mother and real-time occupancy data of departmental resources. The multidimensional physiological parameters include fetal heart rate, uterine contraction pressure, mean arterial pressure, and blood oxygen saturation.

[0020] The data transmission layer 20 is communicatively connected to the physical sensing layer 10. The data transmission layer 20 establishes a bidirectional data channel between the physical sensing layer 10 and the digital twin engine layer 30. The data transmission layer 20 transmits real-time physiological signals and device state vectors collected in the physical space to the digital twin engine layer 30, and simultaneously sends the scheduling instructions generated by the digital twin engine layer 30 to the execution mechanism at the physical end.

[0021] The digital twin engine layer 30 is the core computing unit of the system. The digital twin engine layer 30 includes a physiological load mapping module 31, a speculative execution module 32, an atomic transaction control module 33, a conflict entropy optimization module 34, and a consistency verification module 35.

[0022] The physiological load mapping module 31 receives the set of physiological parameters uploaded by the data transmission layer 20. The physiological load mapping module 31 uses a preset mapping operator to convert the physiological parameters into a resource occupancy probability vector. This vector is used to quantify the potential demand probability of parturients for the emergency operating room, blood bank, and neonatal resuscitation table.

[0023] The speculative execution module 32 is connected to the physiological load mapping module 31. The speculative execution module 32 compares the resource occupancy probability vector with a dynamic threshold. If the probability value exceeds the dynamic threshold, the speculative execution module 32 initiates a speculative branching process within the digital twin space. The speculative execution module 32 is responsible for simulating the pre-execution path before the physical command is issued.

[0024] The atomic transaction control module 33 manages resource locking behavior involved in speculative branching processes. The atomic transaction control module 33 executes a two-phase commit protocol. In the first phase, the atomic transaction control module 33 sends pre-lock requests to each resource node. In the second phase, the atomic transaction control module 33 performs formal latching based on the feedback from the resource nodes. The atomic transaction control module 33 ensures the atomicity of cross-physical domain resource scheduling.

[0025] The conflict entropy optimization module 34 is used to handle resource contention in scenarios involving multiple pregnant women. The module calculates the global conflict entropy based on the priority and resource dependencies of each speculative branch process. It then uses an iterative search algorithm to find the set of scheduling paths that minimizes the global conflict entropy, thereby reconstructing the department's operational flow.

[0026] The consistency verification module 35 is configured at the output of the digital twin engine layer 30. The consistency verification module 35 establishes a monitoring counter and verification points. The consistency verification module 35 compares the actual medical instructions in the physical space with the predicted execution path in the twin space. If a deviation occurs, the consistency verification module 35 triggers a branch rollback protocol, reversing the resource latching operations already performed by the atomic transaction control module 33.

[0027] The application feedback layer 40 includes a visual terminal set up at the medical staff workstation and a mobile terminal worn by medical staff. The application feedback layer 40 receives process optimization suggestions output by the digital twin engine layer 30. The application feedback layer 40 displays the pre-scheduled path confirmed by the consistency verification module 35 and guides medical staff to perform the corresponding medical actions.

[0028] The specific steps of the simulation and optimization method for the operation process of an obstetric emergency department based on digital twin technology provided by this invention are described below: In step S100, the system performs global situational awareness and real-time status synchronization. The physical sensing layer 10 collects a set of multidimensional physiological parameters of the parturient. Multidimensional physiological parameter set Specifically, it can be expressed as follows: .in, This is the fetal heart rate value. This refers to the frequency and intensity of uterine contractions. Mean arterial pressure of the parturient, This refers to blood oxygen saturation. The data acquisition process employs high-frequency sampling technology, with the sampling frequency set according to medical monitoring standards. The specific signal filtering and preprocessing are well-known techniques in the field and will not be elaborated upon here. Simultaneously, the system extracts the departmental resource status matrix. The matrix records the real-time availability or occupancy status of operating rooms, delivery rooms, blood bank inventory, and medical staff.

[0029] In step S200, the system executes the physiological-process load vector mapping logic. The physiological load mapping module 31 calls the preset mapping operator. Processing multidimensional physiological parameter sets Generate resource occupancy probability vector Resource occupancy probability vector Expressed as: .in, Representing the mother on the first The probability of potential demand for specific emergency resources. (In implementation) Corresponding to the probability of demand for emergency cesarean section operating rooms Corresponding to the probability of secondary blood reserve demand. Corresponding to the probability of neonatal resuscitation unit demand. Mapping operator. Based on the correlation analysis of historical clinical case data and process time, probability components are determined by calculating the degree to which current physiological characteristics deviate from the normal baseline.

[0030] In step S300, the system performs speculative branch triggering and cross-physical domain atomic latching. When any component of the resource occupancy probability vector... Satisfying the trigger condition At that time, the speculative execution module 32 generates a speculative branching process within the digital twin engine layer 30. Among these... This represents the dynamic threshold for the corresponding resource. The parameters are adjusted based on the maternal risk rating. Subsequently, the atomic transaction control module 33 initiates the two-phase commit protocol. During the pre-lock request phase, the system sends a query command to the controller of the relevant physical node. The nodes provide feedback on whether their available capacity supports pre-latching. During the latching execution phase, if all involved nodes provide a ready signal, the system performs logical locking at the physical resource control layer to ensure that the resource is not occupied by non-urgent processes within the speculative window period.

[0031] In step S400, the system performs global simulation optimization based on conflict entropy. In a scenario where multiple mothers concurrently trigger speculative execution, the conflict entropy optimization module 34 calculates the global conflict entropy. Global conflict entropy The calculation formula is: In the formula, This represents the current total number of pregnant women under monitoring. The total number of controlled resource types, For the mother Resources Predicted demand probability, This is the timeliness weighting coefficient. Values ​​are assigned based on clinical triage levels (e.g., Level 1 emergency, Level 2 emergency). The conflict entropy optimization module 34 searches for a suitable scheduling sequence by traversing different scheduling sequences within the twin space. The set of optimal paths to obtain the minimum value This set will then be transformed into specific departmental resource scheduling instructions.

[0032] In step S500, the system performs consistency verification and self-healing rollback. The consistency verification module 35 calculates the consistency deviation factor at key process nodes. Consistency Deviation Factor The calculation method is as follows: .in, For physical space in Real-time clinical medical instructions generated at any time These are the speculative instructions executed in the twin space at the corresponding time. If This indicates that the predicted path aligns with the actual medical decision, and the system maintains a resource-locked state until the task is completed. If This indicates a change in the actual clinical pathway, and the system immediately triggers a fallback function. Backoff function Perform an atomic undo operation to release all physical resource capacity locked due to this speculative branch and reset the associated watch counters, returning the physical resources to the available pool.

[0033] Through the iterative process of steps S100 to S500, the system achieves closed-loop control from physiological signal perception to automatic pre-scheduling of processes, and eliminates the waiting time for resource coordination in physical space through speculative execution mechanism.

[0034] Step S2 includes steps S210 to S240. In step S210, the physiological load mapping module 31 transmits the set of physiological parameters to the physical sensing layer 10. Standardization processing is performed. The physiological load mapping module 31 constructs a time-sliding window to convert continuously acquired physiological electrical signals into structured tensors. (The fetal heart rate is used as an example.) For example, the system extracts the mean, variability, and deceleration features within a specific time window. For denoising and feature extraction algorithms for physiological signals, those skilled in the art can employ existing clinical signal processing techniques; their specific implementations are well-known technologies in the field and will not be elaborated upon here.

[0035] In step S220, the physiological load mapping module 31 uses the mapping operator Calculate the deviation vector of each physiological parameter relative to the normal physiological baseline. Deviation vector The expression is: in, . In the formula, These are the currently collected measured values ​​of physiological parameters. This represents the baseline mean of the physiological parameters for the mother at the corresponding gestational week. This represents the standard deviation of the parameter. Deviation vector. This is used to quantify the degree of abnormality in the mother's physiological state, providing a data basis for determining subsequent process branches.

[0036] In step S230, the physiological load mapping module 31 maps the deviation vector Convert to resource occupancy probability vector Mapping operators Includes a multidimensional sensitivity matrix Resource occupancy probability vector The calculation process is expressed as follows: in, . In the formula, The preset sensitivity weight matrix, For bias vectors, For the normalization function, ensure that each component The range of values ​​is within Between. Components The probability of needing an emergency cesarean section operating room is affected by the degree of fetal heart rate deviation. Deviation from uterine contraction pressure The weighted effect is relatively large; the components The corresponding probability of blood preparation needs, its value and the deviation of mean arterial pressure It has a strong correlation.

[0037] In step S240, the physiological load mapping module 31 maps the resource occupancy probability vector. Perform time-series evolution analysis to identify probability growth rate characteristics. The physiological load mapping module 31 calculates the probability change rate vector. If a certain resource occupancy probability component The absolute value exceeds the initial threshold, or its rate of change If the value remains positive and the slope exceeds the set range, the physiological load mapping module 31 determines the vector state as a high-risk trigger state and synchronizes it to the speculative execution module 32.

[0038] Through steps S210 to S240, the physiological load mapping module 31 transforms discrete physiological indicators into resource demand probabilities with business semantics. This mapping mechanism no longer relies on manual triage instructions but directly extracts process change signals from abnormal fluctuations in the physiological dimension, providing a definite triggering basis for the subsequent speculative branching process. In specific implementation, a multi-dimensional sensitivity matrix... The parameters are calibrated offline based on historical sample data from obstetric emergency departments to ensure that probability predictions conform to the general logic of clinical medicine.

[0039] The mapping operator mentioned in this embodiment The deviation calculation and weight synthesis logic of the lower-level components constitute the core means for the digital twin engine layer 30 to perceive physical space risks, supporting the technical solution for physiological drive process optimization in the claims.

[0040] Step S300 further includes steps S310 to S340. In step S310, the system performs a speculative branch trigger determination. The speculative execution module 32 acquires the resource occupancy probability vector output by the physiological load mapping module 31 in real time. The system extracts the target resources. Current dynamic threshold This threshold is determined by multiplying the basic threshold of the departmental resource management system by the current overall resource occupancy rate. Simultaneously, the system incorporates a maternal risk correction factor. This factor is assigned a value based on the mother's clinical classification; the higher the classification, the higher the value. The smaller the value, the higher the sensitivity of pre-execution triggering for high-risk pregnancies. When the decision formula... Upon establishment, the speculative execution module 32 creates a parallel process branch within the digital twin space to simulate the subsequent business flow where the resource is occupied.

[0041] In step S320, the atomic transaction control module 33 initiates the first phase of a two-phase commit protocol for cross-physical domain resources, namely the pre-lock request phase. The digital twin engine layer 30 then sends requests to multiple physical resource nodes involved in this speculative branch. Send pre-lock command . This includes the inferred task's unique identifier, estimated duration, and priority weight. (Each physical resource node...) After receiving the instruction, the controller follows the formula. Perform availability verification. Among these, For nodes Total physical capacity This represents the total amount of pre-latched capacity that has already taken effect. This is a preset security redundancy threshold. If the verification passes, the node returns a ready response code. The system temporarily suspends these resources in the node's local memory. For the specific logic implementation of the physical node controller, those skilled in the art can use existing distributed system communication protocols; the specific handshake process is well-known in the field and will not be elaborated upon here.

[0042] In step S330, the atomic transaction control module 33 executes the second phase of the two-phase commit protocol, namely the atomic latch phase. The system collects all participating nodes. The feedback results are used to calculate the global latch decision factor. Only when all nodes provide feedback... Signal, that is, satisfying At that time, the atomic transaction control module 33 sends execution latch instructions to each node. Each node controller receives... Following the instruction, the resource is officially marked as locked in the physical device's control logic, blocking access requests from non-urgent tasks. If any node reports... If no response is received within the specified time, the system will execute a global cancellation command to release the feedback. The suspended state of a node. This atomic transaction mechanism ensures the overall consistency of related resources being ready synchronously in the obstetric emergency process, avoiding process interruptions caused by a single resource waiting for other resources to be released.

[0043] In step S340, the system establishes a mapping relationship between speculative latch states and physical actuators. The atomic transaction control module 33 sends the successfully latched resource identifier and the corresponding expected path to the application feedback layer 40. The atomic transaction control module 33 issues a highest-priority elevator call command to the automatic elevator control system via the data transmission layer 20, and simultaneously pushes a pre-preparation task description to the medical staff terminal. This step ensures that the necessary physical resources are in a ready and exclusive state before the doctor issues the final written medical order, thereby eliminating the physical time consumption caused by manual application, approval, and allocation in traditional processes.

[0044] Through steps S310 to S340 described above, this invention achieves probabilistic prediction-based forward-looking resource allocation. This mechanism integrates discrete physical resource management into atomic logical transactions, effectively solving the technical problem of delayed multi-departmental collaboration in obstetric emergencies. The definitions and logic of atomic transactions, pre-locking requests, and global decision factors described above provide specific and sufficient technical support for the speculative dynamic reconfiguration described in the claims.

[0045] Step S400 includes steps S410 to S440. In step S410, the conflict entropy optimization module 34 calculates the timeliness weight coefficients involved in each speculative branch process. This coefficient characterizes the parturient's condition. Resources The urgency of the need and its corresponding clinical risk weight. Timeliness weight coefficient. The lower-level implementation logic is determined based on the obstetric triage standards, and the specific calculation method is as follows: in, The risk weight score for pregnant women is assigned in a stepwise manner according to the triage level (e.g., Level I: critically ill, Level II: unstable vital signs); This is a resource scarcity factor, determined based on the real-time idle rate of the department's current resources.

[0046] In step S420, the conflict entropy optimization module 34 constructs a global conflict entropy model to quantify the disorder level of the current department's operational status. Global Conflict Entropy The calculation formula is as follows: In the formula, The total number of currently active maternal entities within the digital twin space; This represents the total number of controlled resource types included in the system. The output of the physiological load mapping module 31 in step S230 The first pregnant woman Real-time predicted occupancy probability of resource types. The higher the value, the more intense the competition for departmental resources, and the higher the risk of process blockages or delays.

[0047] In step S430, the conflict entropy optimization module 34 uses an iterative search algorithm to deduce different resource scheduling sequences within the digital twin engine layer 30. The system aims to minimize the global conflict entropy, and, while satisfying the predicted branches for all high-risk pregnancies, searches for the optimal set of paths. : In the formula, Assign links to candidate resources. The time sequence of the corresponding paths is arranged accordingly. During the search process, the system automatically lowers the priority of non-urgent speculative branches and optimizes the overlap of core paths. For the specific implementation of heuristic search or greedy algorithms, those skilled in the art can choose according to computational performance requirements; the algorithm details are well-known in the field and will not be elaborated here.

[0048] In step S440, the conflict entropy optimization module 34 optimizes the optimal path set. A dynamic process reconfiguration instruction set is generated. This instruction set includes: adjusting the operating room preparation sequence, replanning the stretcher transport route, and dynamically adjusting the blood bank dispensing order. The conflict entropy optimization module 34 issues the reconfiguration instruction to the atomic transaction control module 33, which then executes the subsequent resource latching or releasing operations. This optimization mechanism based on minimizing entropy transforms isolated individual predictions into global group optimization, ensuring the scientific and logical consistency of departmental resource allocation under extreme load scenarios such as the resuscitation of multiple pregnant women.

[0049] Through steps S410 to S440, this embodiment discloses how to dynamically mitigate process conflicts using a mathematical model. This process goes beyond simply predicting single-point paths; it achieves real-time balancing of the dynamic load across the entire department through a conflict entropy model. The detailed disclosure of the above technical features clarifies the specific logical path for process simulation optimization in the claims, providing ample support for the dynamic reconstruction of obstetric emergency processes.

[0050] Step S500 further includes steps S510 to S550, in which the consistency verification module 35 configures a watch counter in the speculative branching process within the digital twin space. Consistency checkpoints Consistency checkpoints These are deployed at key decision-making points in the department's workflow, such as the secondary triage confirmation before a pregnant woman enters the operating room, or the moment a doctor issues formal medical orders in the electronic medical record system. Monitoring counters. This counter is used to record the duration of speculative branching procedures, and its initial value is set based on the routine preparation time for the corresponding medical procedure. If the counter value exceeds the preset survival time threshold... If no feedback signal is received from the physical space, the system will automatically mark the speculative branch as invalid.

[0051] In step S520, the system performs instruction comparison to calculate consistency deviation. The consistency verification module 35 captures the actual medical instructions generated in the physical space in real time through the data transmission layer 20. At the same time, the system invokes the digital twin engine layer 30. Speculative instructions being executed at any given time Consistency Deviation Factor The calculation method is expressed as follows: in, This represents the instruction residual under the second norm. Instruction vector. Includes the target department code, resource type identifier, operation type code, and priority parameters. When, it is determined to be a consistent hit; when When this occurs, it is determined to be a prediction deviation.

[0052] In step S530, the system performs branch confirmation based on the deviation determination result. If consistency is achieved, the consistency verification module 35 sends a status transition command to the atomic transaction control module 33. The atomic transaction control module 33 converts the pre-latched state established in step S330 into a formally occupied state and updates the resource state matrix. At this point, the resource locking that was originally in the speculative execution phase (such as locked blood reserves and elevators that have been moved to designated floors) is seamlessly integrated into the physical medical process, eliminating the physical time lag in resource allocation.

[0053] In step S540, the system initiates a self-healing backoff protocol in the event of a prediction deviation. When a deviation is detected... At that time, the consistency verification module 35 immediately triggers the rollback function. Backoff function The execution process encompasses the following lower-level technical aspects: 1. Atomic Transaction Reversal: Atomic transaction control module 33 sends a notification to all involved physical resource nodes. Send back signal .

[0054] 2. State Reset: After receiving the rollback signal, the physical node controller clears the pre-latched placeholders in memory and sets the corresponding device capacity. Restore to the available resource pool.

[0055] 3. Logical Reset: The digital twin engine layer 30 destroys the corresponding speculative branch process entity and releases computing resources.

[0056] This process ensures that when a doctor changes their clinical decision (e.g., from cesarean section to continued observation), the system can instantly release the pre-locked operating room and medical resources, preventing resource deadlock or ineffective occupation. For the specific implementation of distributed database state rollback, those skilled in the art can use existing transaction log rollback technology; its specific details are well-known in the field and will not be elaborated here.

[0057] In step S550, the system performs a second dynamic balancing of global resources after the rollback. After completing the rollback operation, the consistency verification module 35 feeds back the latest resource availability status to the conflict entropy optimization module 34. The conflict entropy optimization module 34 then recalculates the global conflict entropy. Furthermore, based on the current physical conditions, the system adjusts the weights of other existing speculative branch processes. This dynamic feedback mechanism ensures that the system can maintain optimal scheduling of departmental resources even when faced with changes in medical decisions.

[0058] Through steps S510 to S550, this invention constructs a process refactoring system with self-healing capabilities. This system not only solves the problem of resource misuse that may arise from "pre-execution," but also ensures a high degree of synchronization between the digital twin system and physical medical procedures through mathematical verification and rollback logic. The above description in the specification provides a clear implementation path and technical support for the technical features of consistency verification and rollback protocols in the claims.

[0059] Due to the high degree of uncertainty in the obstetric emergency environment, the atomicity and timeliness of the aforementioned rollback mechanism are crucial to ensuring the safe operation of the department. This embodiment defines a consistency deviation factor. With backoff function At the technical level, it enables dynamic correction of complex medical processes, representing a substantial technological improvement.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A simulation and optimization method for the operational process of an obstetric emergency department based on digital twin technology, comprising the following steps: Step S100: Collect multidimensional physiological parameters of the parturient in real time and real-time occupancy status data of departmental resources, and extract the resource status matrix that records the resource status. Step S200: Use a preset mapping operator to process the multidimensional physiological parameter set and generate a resource occupancy probability vector that reflects the probability of the parturient's potential demand for specific emergency resources; Step S300: When any component in the resource occupancy probability vector meets the preset trigger judgment condition, a speculative branch process is generated in the digital twin space, and a two-phase commit protocol for cross-physical domain resources is executed through the atomic transaction control mechanism to perform logical locking on the physical resources. Step S400: In the scenario where multiple mothers concurrently trigger speculative branching processes, calculate the global conflict entropy based on the resource occupancy probability vector to quantify the degree of resource competition disorder, find the optimal set of paths that minimizes the global conflict entropy through a search algorithm, and generate dynamic process refactoring instructions. Step S500: Calculate the consistency deviation factor by comparing the actual medical instructions in the physical space with the inferred instructions in the digital twin space, and execute the resource status correction or trigger the self-healing rollback protocol based on the consistency deviation factor.

2. The method according to claim 1, characterized in that, In step S200, the process of generating the resource occupancy probability vector includes: A time-sliding window was constructed to convert fetal heart rate, uterine contraction pressure, mean arterial pressure, and blood oxygen saturation signals into structured data; Calculate the deviation of each physiological parameter from the baseline mean value at the corresponding gestational week and generate a deviation vector; The deviation vector is weighted and synthesized using preset sensitivity weights and bias parameters to obtain a resource occupancy probability vector that includes the demand probability of emergency operating rooms, blood banks, and neonatal resuscitation units.

3. The method according to claim 1, characterized in that, In step S300, the triggering condition is determined by the product of the dynamic threshold of the target resource and the maternal risk correction factor. The dynamic threshold is dynamically adjusted based on the real-time occupancy rate of departmental resources, and the risk correction factor is set based on the clinical risk rating of the mother.

4. The method according to claim 1, characterized in that, In step S300, the pre-lock request phase of the two-phase commit protocol includes: Send a pre-lock command containing the task identifier and the expected duration to the physical resource node involved in the speculative branching process; Physical resource nodes perform availability checks based on their total capacity, pre-locked capacity, and security redundancy requirements, and then respond with a readiness status.

5. The method according to claim 4, characterized in that, The atomic latch phase of the two-phase commit protocol includes: Collect feedback results from all physical resource nodes and calculate the global latching decision state; When all involved nodes have reported that they are ready, the target resource is marked as locked in the control logic of the physical device. If a node sends a rejection signal or fails to respond, a global cancellation operation is performed.

6. The method according to claim 1, characterized in that, In step S400, the process of calculating the global conflict entropy includes: Risk weights are assigned based on the clinical triage level of the mother, and resource scarcity factors are determined based on the real-time resource availability rate. The timeliness weight coefficients of each speculative branch process are obtained by combining the risk weight and the scarcity factor. Based on the logarithmic distribution relationship between the timeliness weight coefficient and the resource occupancy probability vector, a global conflict entropy reflecting the resource competition status of the entire domain is synthesized.

7. The method according to claim 1, characterized in that, In step S500, the process of calculating the consistency deviation factor includes: Configure watch counters and consistency checkpoints at key decision nodes in speculative branching processes; Capture the actual medical instructions in the physical space at the verification point, and retrieve the inferred instructions in the digital twin space at the corresponding moment; The consistency deviation factor is obtained by quantitatively calculating the residual difference between the actual medical instruction vector and the inferred instruction vector.

8. The method according to claim 7, characterized in that, The execution process of the self-healing rollback protocol includes: When the consistency deviation factor reflects a deviation from the instruction, the rollback procedure is triggered; Send a rollback signal to the physical resource nodes involved, clear the pre-latched placeholders in the node controller memory and restore the available capacity of the physical devices; Destroy the corresponding speculative branch process entity within the digital twin space and release computing resources.

9. The method according to claim 8, characterized in that, After executing the rollback procedure, the system recalculates the global conflict entropy based on the latest physical resource availability and performs weight correction on other surviving speculative branch processes.

10. A simulation and optimization system for the operational process of an obstetric emergency department based on digital twin technology, characterized in that: include: The physical sensing layer is used to collect the physiological parameters of the mother and the occupancy status of departmental resources; The data transmission layer is used to establish a data interaction channel between the physical space and the digital twin space; The digital twin engine layer includes a physiological load mapping module, a speculative execution module, an atomic transaction control module, a conflict entropy optimization module, and a consistency verification module, used to execute the method as described in any one of claims 1 to 9; The application feedback layer is used to display process optimization suggestions, guide resource scheduling, and present consistency verification results.

Images