Emergency platform work order management method based on digital twin and artificial intelligence

Abstract

The present invention proposes a work order management method for an emergency platform based on digital twins and artificial intelligence, which belongs to the field of information technology application innovation technology. The method includes: collecting historical emergency data and real-time data of emergency scenes to build a task scheduling system based on knowledge graphs; decomposing tasks into several subtasks according to work order data and allocating the best combination of emergency resources; setting optimization strategies to coordinate the allocation and control of each resource to optimize task allocation; monitoring the optimized task allocation in real time, and combining the real-time data stream of real-time monitoring to ensure that the dynamic environment changes at any time during the task execution process; re-entering the optimized task allocation and resource scheduling strategies into the knowledge graph for closed-loop optimization. The present invention effectively overcomes the problems of inaccurate task scheduling, weak real-time response capabilities, and low resource utilization in the existing emergency platform work order management system, and greatly improves the overall efficiency and automation level of the emergency platform.

Description

Technical Field

[0001] The present invention belongs to the field of information technology application innovation technology, and in particular relates to a first aid platform work order management method based on digital twins and artificial intelligence. Background Art

[0002] With the development of information technology, emergency medical platforms are playing an increasingly important role in the modern healthcare system. Traditional emergency medical platform work order management systems typically rely on manual scheduling and fixed rule-based algorithms for resource allocation and task scheduling. However, the complexity, variability, and urgency of emergency medical tasks make these traditional methods problematic in practical applications. This is especially true when faced with multiple concurrent tasks, frequent emergencies, resource shortages, or unpredictable factors (such as traffic conditions and equipment failures). Traditional systems struggle to ensure efficient and accurate task allocation.

[0003] Most existing emergency work order management systems use a fixed-priority scheduling mechanism, which is unable to respond to changes in task urgency and resource availability in real time, often leading to uneven or untimely resource allocation. For example, when multiple emergency tasks occur simultaneously, the system often fails to dynamically adjust priorities and optimize scheduling strategies, resulting in some tasks not receiving a timely response, affecting overall emergency response efficiency.

[0004] Furthermore, resource scheduling and task allocation primarily rely on human dispatchers, who must manually judge and assign each task based on their experience. This not only increases the dispatcher's burden but can also lead to human error, especially during high-pressure emergency response. Emergency response resources, such as personnel, equipment, and vehicles, are highly interconnected. For example, different emergency personnel possess different skills, and different equipment has specific functions. Current systems are unable to fully model and understand these complex resource relationships, resulting in low utilization of emergency resources.

[0005] In addition, another limitation of existing technologies is the lack of effective processing capabilities for real-time dynamic data. With the advancement of urbanization, unpredictable factors such as traffic congestion and weather changes often have a significant impact on the timeliness of emergency tasks, while traditional systems rely only on historical data and static rules and cannot be dynamically optimized in combination with real-time data. This means that emergency vehicles may encounter unexpected delays during the execution of their tasks, and the system cannot respond in a timely manner and cannot dynamically adjust the allocation of work orders or task paths. Furthermore, traditional work order management systems lack the ability to perform fine-grained decomposition of the complexity of different tasks, and cannot automatically match resources according to the specific needs of each subtask (such as personnel skills, equipment configuration, etc.), resulting in poor coordination during the execution of emergency tasks, and often waste of resources or unreasonable allocation. Summary of the Invention

[0006] The purpose of this invention is to design a first aid platform work order management method based on digital twins and artificial intelligence, use the application of information technology (information technology application innovation) in the first aid platform, and improve the intelligence level and emergency response capability of the first aid platform through autonomous and controllable technologies and solutions. It effectively overcomes the problems of inaccurate task scheduling, weak real-time response capability, and low resource utilization in the existing first aid platform work order management system, and greatly improves the overall efficiency and automation level of the first aid platform.

[0007] In order to achieve the above objectives, the present invention provides a method for managing emergency platform work orders based on digital twins and artificial intelligence, the method comprising the following steps:

[0008] Step 1: Collect historical and real-time emergency data from emergency scenarios to build a knowledge graph, and then build a task scheduling system based on the knowledge graph, including:

[0009] S11. Obtain historical emergency data and real-time data to form total data;

[0010] S12. Treat the emergency resources and tasks in the total data as nodes in the knowledge graph. The nodes are connected by relationship edges to represent the potential matching relationship between resources and tasks. Each relationship expresses the degree of association between nodes through a dynamic weight.

[0011] S13. For each task, calculate the comprehensive matching degree between each task and various emergency resources to select the optimal resource allocation. At the same time, design an urgency adjustment coefficient for each task to ensure that high-urgency tasks are allocated resources first;

[0012] S14. Calculate work order data including optimal resource allocation and optimal task priority;

[0013] Step 2: Decompose the task into several subtasks based on the work order data and assign the optimal combination of emergency resources to each subtask, including:

[0014] S21. Automatically generate work orders based on the reasoning and matching results of the knowledge graph;

[0015] S22. Decompose each emergency task into several subtasks, and then allocate the optimal resource combination to each subtask based on the comprehensive matching degree and urgency adjustment coefficient;

[0016] S23. After the work order for each subtask is generated, all tasks are sorted according to the comprehensive matching degree of the subtasks and the urgency adjustment coefficient to ensure that the most urgent tasks are responded to first, and the final work order is output;

[0017] Step 3: For the ever-changing emergency scenarios, set up a multi-agent-based optimization strategy to coordinate the allocation and control of each resource and optimize task allocation, including:

[0018] S31. Define multiple agents including: vehicle agent, medical staff agent, and equipment agent;

[0019] S32. Design the state space and reward mechanism of multiple agents based on the real-time scheduling requirements of the emergency platform to conduct multi-intelligence control. At the same time, design a resource coordination mechanism to optimize task allocation through game and negotiation mechanisms between resources. Design a resource conflict resolution mechanism to address conflicts between emergency tasks and ensure maximum resource allocation efficiency.

[0020] S33. After multiple rounds of agent learning and collaborative game, all resource allocation plans will eventually converge to the optimal state and output;

[0021] Step 4: Monitor the optimized task allocation in real time. Combined with the real-time data stream of real-time monitoring, ensure that the dynamic environment changes can be responded to at any time during task execution, including:

[0022] S41. Collect real-time data streams and convert them into time series for modeling. The real-time data streams contain multiple events that occur in different time periods. Different impact functions are designed for each event's impact on the task to measure the impact of the event. Then, the priority of the task is dynamically adjusted based on the impact of each event on the task.

[0023] S42. Design dynamic adjustment rules based on real-time data streams to ensure that task execution and resource allocation are always optimized;

[0024] S43. Design an event-task association mechanism to assess the actual impact of events on tasks and trigger corresponding adjustments. When the event impact index exceeds a certain threshold, the adjustment mechanism will be automatically triggered.

[0025] S45. Output the optimized and adjusted work order;

[0026] Step 5: Re-enter the optimized task allocation and resource scheduling strategies into the knowledge graph for closed-loop optimization, including:

[0027] Build a feedback model, take task execution feedback as input, and adjust future work order scheduling strategies in real time by comparing the actual task execution with the plan.

[0028] Furthermore, the structure of the knowledge graph includes:

[0029] The nodes include:

[0030] Vehicle node V(i) represents the i-th emergency vehicle;

[0031] The medical staff node P(j) represents the jth medical staff;

[0032] The equipment node E(k) represents the kth emergency equipment;

[0033] Task node T(l) represents the lth emergency task;

[0034] Relationships include:

[0035] Define the relationship between various resources and tasks, and use R(V,T), R(P,T), and R(E,T) to represent the matching relationship between each resource and task.

[0036] Furthermore, the S13 specifically includes:

[0037] Each task T(l) needs to be matched with multiple resources, and the comprehensive matching degree between resources and tasks is calculated. To select the optimal resource allocation, it is expressed as follows:

[0038]

[0039] in, Represents task T(l) and resource R i degree of matching; Represents resource R i The degree of matching with a specific attribute of task T(l); w k represents the weight of attribute k, indicating the importance of different attributes; λ·ΔD(T l ,R i ) represents the innovative real-time distance regularization term, and represents the resource R i The difference in geographical distance from task T(l), ΔD(T l ,R i ) is the geographical distance between the task and the resource, λ is the regularization coefficient; -γ·C(R i ) represents the resource load penalty term, C(R i ) is the current resource load level;

[0040] The matching degree calculation for emergency tasks needs to take into account the urgency of the task. Therefore, a dynamic adjustment item is designed for each task to ensure that resources are allocated to high-urgency tasks first, as shown below:

[0041]

[0042] Among them, μ l Indicates the dynamic priority adjustment coefficient of the lth task. l It represents the urgency of the lth task, and the value of U is determined by the task emergency assessment model; Indicates the sum of the urgency of all current tasks.

[0043] Furthermore, the optimal resource combination is allocated to each subtask based on the comprehensive matching degree and the urgency adjustment coefficient, which is expressed as follows:

[0044]

[0045] Among them, R(T(l) k ) represents subtask T(l) k the set of resources allocated; Represents resource R i With subtask T(l) k The matching degree of μ l represents the urgency weight of task T(l);

[0046] In S22, to ensure reasonable resource allocation and avoid resource conflicts or duplication, task allocation needs to follow the following rules:

[0047] Each resource can be assigned to only one task or subtask, unless the resource can be used simultaneously;

[0048] Automatically check the current load of resources to avoid over-allocation of resources. If a resource is overloaded, its priority will be lowered by using the load penalty item in the matching degree.

[0049] When there is a resource conflict, the needs of urgent tasks are met first, and low-priority tasks are delayed.

[0050] Furthermore, the final work order is a task-resource allocation table, including the subtasks of each task, the allocated resources, and the priority order of each task. The final work order structure is represented as follows:

[0051] Task T(l): Task type, urgency μ l , geographical location;

[0052] Subtask T(l) k :Detailed description of each subtask; corresponding allocation resources R(T(l) k );

[0053] Task priority sorting: Tasks will be processed in descending order based on the task priority calculated by the priority formula.

[0054] Furthermore, the S32 specifically includes:

[0055] The state space and reward mechanism of the intelligent agent are designed to address the urgency and complexity of task scheduling. The state space is represented as follows:

[0056]

[0057] Among them, L i Represents resource R i The current load of U i Represents resource R i availability, indicating whether the resource is idle; μ l represents the urgency adjustment coefficient of task T(l); D pos (T(l),R i ) represents the task T(l) and the resource R i geographical distance; Indicates that the resource R i Collaboration status with other resources;

[0058] The reward mechanism is as follows:

[0059]

[0060] Among them, α·μ l Indicates the emphasis on task urgency; α controls the priority increase of urgent tasks; represents the matching weight between resources and tasks, which is used to reward resource agents that can complete tasks efficiently; δ·C collab (R i ) represents the collaborative work reward item, which is used to consider the positive feedback of cooperation between resources; γ·C(R i ) represents the resource load penalty term, which prevents resources from being overused or participating in tasks for a long time;

[0061] The collaboration mechanism is expressed as follows:

[0062] CollaborativeReward(R i ,R j )=C collab (R i ,R j )=λ·Efficiency(R i ,R j )-θ·D pos (R i ,R j )

[0063] Among them, λ represents the weight of collaborative efficiency, which indicates the efficiency of cooperation between resources; θ represents the distance penalty term, which means that if the distance between two resources is far, the collaborative benefit will be reduced; Efficiency (R i ,R j ) indicates resource R i and R j The efficiency of cooperation on tasks is calculated using historical data;

[0064] The resource conflict resolution mechanism is as follows:

[0065]

[0066] in, and Represents resource A i and A j With task T(l) k The matching degree of μ l Represents the urgency adjustment coefficient of task T(l).

[0067] Furthermore, all resource allocation schemes will eventually converge to the optimal state and be output, where the output content includes:

[0068] The final resources assigned to each task, along with task urgency and resource coordination, as well as specific resource execution paths, ensure that tasks can be completed according to priority and time sensitivity.

[0069] Furthermore, the priority of the task is dynamically adjusted according to the impact of each event on the task, as shown below:

[0070]

[0071] Among them, μ′ l represents the task priority adjusted after real-time event stream processing; μ l Indicates the original task priority; Δμ l (e i (t)) represents each event e i (t) The increment to the task priority.

[0072] Furthermore, the design of dynamic adjustment rules based on real-time data streams includes:

[0073] When the traffic situation changes, through event E traffic (t) Recalculate the path to ensure that resources can arrive at the task location on time. The path adjustment formula is as follows:

[0074] P′(T(l))=P(T(l))+ΔD(E traffic (t))

[0075] Among them, P(T(l)) represents the original path of task T(l); ΔD(E traffic (t)) represents the traffic event E traffic (t) is the triggered path or time increment; P′(T(l)) represents the optimal path after dynamic adjustment;

[0076] When the device status event E deviceWhen (t) occurs, the affected device is dynamically replaced. The device replacement formula is as follows:

[0077] R′(T(l) k )=R(T(l) k )-E fault +E backup

[0078] Among them, R(T(l) k ) represents the current subtask T(l) k Resource allocation; E fault Indicates a faulty device; E backup Represents a standby device that automatically replaces the affected resource.

[0079] In emergency scenarios, patients may be transferred to different locations, requiring the nearest resources to be reallocated. The resource reallocation formula is as follows:

[0080]

[0081] in, Represents task T(l) and resource R i The matching degree of D pos (T(l),R i ) represents the new location of task T(l) and resource R i The geographical distance; R′(T(l)) represents the resource allocation after dynamic adjustment;

[0082] The association mechanism between events and tasks is as follows:

[0083]

[0084] Where I(T(l),E(t)) represents the overall impact index of event E(t) on task T(l); w i represents the weight, based on the urgency of the event and the relevance of the task; δ(T(l),e i (t)) represents the specific impact of the event on the task.

[0085] Furthermore, the feedback model is expressed as follows:

[0086] ΔT′(l)=λ·(P actual (T(l))-P′(T(l)))+γ·F(t)

[0087] Where ΔT′(l) represents the improvement in task execution effect, which is used to adjust future task orders; λ represents the path optimization coefficient, which weighs the difference between the planned path and the actual path; P actual(T(l)) represents the actual execution path of the task; P′(T(l)) represents the optimized planned path; γ represents the feedback weight coefficient, which measures the impact of feedback data on task adjustment; F(t) represents the execution feedback data.

[0088] The beneficial technical effects of the present invention are at least as follows:

[0089] In order to overcome the above-mentioned shortcomings of the existing first aid platform work order management system, the present invention proposes a first aid work order dynamic generation and intelligent allocation system based on knowledge graph, multi-agent system (MAS) and real-time event stream processing technology, aiming to solve the problems of inaccurate first aid resource scheduling, poor real-time performance, and high manual dependence in the existing technology. The system models the complex correlation between first aid resources and tasks by introducing knowledge graphs, and realizes the automated processing of task decomposition and resource matching. At the same time, the multi-agent system uses reinforcement learning technology to conduct games and learning in a digital twin environment to achieve collaborative optimization and scheduling of resources; and real-time event stream processing ensures that the system can dynamically adjust the priorities of work orders and tasks in combination with real-time data (such as first aid calls, traffic conditions, equipment status, etc.) to ensure that first aid tasks are processed in a timely manner.

[0090] First, the present invention constructs a knowledge graph of the first aid platform to semantically express and reason about the complex relationship between first aid tasks and resources (such as vehicles, medical staff, equipment, etc.). Compared with traditional fixed rule systems, the present invention can automatically understand and match the specific requirements of tasks (such as the medical staff skills and equipment types required for different tasks), achieve more accurate work order generation and task decomposition, and greatly improve the rationality and coordination of resource allocation. This not only reduces the need for human intervention and the burden on dispatchers, but also effectively avoids the waste of resources and irrational allocation.

[0091] Secondly, the present invention introduces a multi-agent system, enabling collaborative optimization and scheduling of emergency resources during multi-tasking. Each emergency resource (such as vehicles and medical personnel) acts as an independent agent, continuously adjusting its scheduling decisions through a reinforcement learning algorithm. This ensures that in emergency situations, the system can dynamically generate or adjust work orders based on task priority and automatically allocate the optimal resource combination. Furthermore, through the game mechanism of reinforcement learning, the system can optimize the action strategies of each agent in real time to ensure that resource scheduling is optimal overall.

[0092] Finally, the present invention solves the problem of the inability of existing systems to respond to environmental changes in a timely manner through real-time event stream processing technology. During the execution of emergency tasks, the system can analyze dynamic data from different sources (such as traffic conditions, ambulance driving status, weather changes, etc.) in real time, and dynamically adjust the generation of work orders and resource allocation based on changes in these data. This mechanism ensures that the generation of work orders and resource scheduling for emergency tasks can not only adapt to changes in emergencies, but can also continuously optimize paths and resource allocation during the execution of tasks, thereby improving the response speed and processing efficiency of emergency tasks.

[0093] In summary, through the application of information and communication technologies (artificial intelligence, digital twins), the first aid platform can overcome the limitations of traditional work order management systems and achieve more efficient, intelligent, and reliable first aid task scheduling and resource management. Technical means such as intelligent scheduling algorithms, real-time data processing, resource modeling and optimization, automated task management, and visual decision support not only improve the response speed and processing efficiency of first aid tasks, but also reduce human errors and resource waste, providing strong support for the modern medical system. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0095] Figure 1 This is a flow chart of the first aid platform work order management method based on digital twins and artificial intelligence in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0097] In one or more embodiments, Figure 1 As shown, the present invention discloses a first aid platform work order management method based on digital twins and artificial intelligence, and the method includes steps one to five, including:

[0098] Step 1: Collect historical emergency data and real-time data of emergency scenarios to build a knowledge graph, and build a task scheduling system based on the knowledge graph.

[0099] Specifically, in the scenario of the emergency platform, the present invention has a large amount of historical emergency data and real-time data, which include vehicle location, equipment status, medical staff skills, task urgency, etc. These data inputs will become the basis for building a knowledge graph.

[0100] Among them, the input data of the knowledge graph is:

[0101] Emergency vehicle data: vehicle type, real-time location, load capacity, equipment status, etc.

[0102] Medical staff data: Each medical staff member's skill level, previous experience, availability, location, etc.

[0103] Equipment status data: equipment availability, model, remaining life, etc.

[0104] Task attributes: task urgency, task type (such as cardiac arrest, traffic accident), task location, etc.

[0105] These inputs will serve as the data source for node attributes in the knowledge graph, and semantic modeling will be performed on this basis.

[0106] Furthermore, the present invention abstracts emergency resources (vehicles, medical personnel, equipment, etc.) and tasks (task type, urgency, geographic location, etc.) into nodes in a knowledge graph. Nodes are connected by edges, representing potential matches between resources and tasks. Each relationship expresses the degree of association between nodes through dynamic weights, which are calculated using a specific algorithm.

[0107] node:

[0108] Vehicle node V(i): represents the i-th emergency vehicle, and its attributes include real-time location, load capacity, equipment status, etc.

[0109] Medical staff node P(j): the jth medical staff, with attributes including skill level, availability, experience, etc.

[0110] Equipment node E(k): the kth emergency equipment, including attributes such as equipment model, remaining life, availability, etc.

[0111] Task node T(l): the lth emergency task, which contains attributes such as task urgency, task type, geographical location, etc.

[0112] Relationships: Define the relationships between various resources and tasks, such as whether a vehicle can respond to a task, whether the equipment meets the task requirements, and whether the medical staff's skills are suitable for the current task. Use R(V,T), R(P,T), and R(E,T) to represent the matching relationship between each resource and task.

[0113] Furthermore, for each task, the present invention calculates the matching degree of all associated resources (vehicles, medical personnel, and equipment). This requires designing a unique calculation formula for each matching task. This formula not only considers static resource attributes (such as skills and equipment model) but also dynamically weights them based on real-time data (such as equipment status and task location). New regularization and innovation terms are introduced to improve the rationality of the matching degree.

[0114] Furthermore, each task T(l) needs to be matched with multiple resources, and the comprehensive matching degree between resources and tasks is calculated. To select the optimal resource allocation. The present invention proposes the following formula to calculate the matching degree:

[0115]

[0116] in, Represents task T(l) and resource R i (such as vehicles, medical staff, equipment). Represents resource R i The degree of matching with a specific attribute of the task T(l). For example, the matching degree between the medical staff’s skills and the skills required by the task, the matching degree between the equipment model and the task requirements, etc. k The weight of attribute k represents the importance of different attributes. This weight can be obtained through regression model training based on historical data, or it can be manually set based on expert experience. l ,R i ) represents the innovative real-time distance regularization term, and represents the resource R i The difference in geographical distance from task T(l), ΔD(T l ,R i ) is the geographical distance between the task and the resource, and λ is the regularization coefficient. The closer the distance, the higher the matching degree, and the resource is allocated first. i ) represents the resource load penalty term, C(R i ) is the current resource load, such as whether the ambulance is fully loaded, the workload of medical staff, etc., γ is the penalty coefficient. The higher the load, the lower the matching degree.

[0117] Furthermore, the urgency of the task must be considered in the calculation of the matching degree of the emergency task. The present invention designs a dynamic adjustment item for each task to ensure that high-urgency tasks are allocated resources first.

[0118]

[0119] Among them, μ l Indicates the dynamic priority adjustment coefficient of the lth task. lIndicates the urgency of the lth task. The value of U is determined by the task urgency assessment model (such as a 0-1 score). Indicates the sum of the urgency of all current tasks.

[0120] This formula ensures the relative importance of the urgency of the task in the entire task queue, and dynamically increases the priority of resource allocation for more urgent tasks.

[0121] Furthermore, through the aforementioned matching calculation and dynamic adjustment of task priorities, the present invention ultimately achieves the optimal match between each task and resource. After sorting by matching results, the system outputs work order data containing resource allocation and task priorities. This work order data serves as the basis for subsequent task allocation and scheduling optimization.

[0122] Output work orders: including task T(l) and its assigned vehicle V(i), medical staff P(j), equipment E(k), and detailed information such as the assigned time and execution route. Each work order is generated based on the matching degree and priority calculation mentioned above.

[0123] This step, one of the core components of the emergency response platform, builds a dynamic resource allocation and task priority management system based on a knowledge graph. By introducing a real-time distance regularization term and a resource load penalty term, this effectively addresses the issue of traditional matching models neglecting real-time considerations and resource load. Dynamic adjustment of urgency ensures that emergency response can maximize adaptability to changing circumstances.

[0124] Step 2: Decompose the task into several subtasks based on the work order data and allocate the optimal combination of emergency resources to each subtask.

[0125] Specifically, the input of this step comes directly from the output of step 1. Specifically, it includes:

[0126] Task node T(l): Each task T(l) is generated by the knowledge graph in step 1, and contains attributes such as urgency, task type, and task location.

[0127] Resource Node R i : Resources include vehicles V(i), medical staff P(j) and equipment E(k). These nodes come from the knowledge graph and have real-time status attributes (such as load, skill level, geographic location, etc.).

[0128] Match This is the task T(l) and resource R calculated from step 1 based on real-time distance, resource load, task requirements, etc. i comprehensive matching degree.

[0129] Task urgency adjustment coefficient μ l: The urgency weight of task T(l), determined by the dynamic adjustment in step 1.

[0130] These inputs will be used to generate emergency work orders, broken down into subtasks, and resources will be allocated.

[0131] Furthermore, the work order generation process is based on the reasoning and matching results of the knowledge graph. At this point, the tasks have been associated based on the matching and priority information. The present invention decomposes the tasks into subtasks and allocates the most suitable resources to each subtask.

[0132] Furthermore, each emergency task T(l) needs to be decomposed into several subtasks. The task decomposition strategy depends on the type and requirements of the task. Common task decomposition methods include:

[0133] Rescue mission: usually includes on-site first aid, patient transfer and equipment use.

[0134] Furthermore, the representation of subtasks is:

[0135] T(l)={T(l)1,T(l)2,…,T(l) n}

[0136] Among them, T(l) k Represents the kth subtask. For example, for task T(1) (cardiac arrest), it can be decomposed into: T(1)1: on-site first aid. T(1)2: patient transfer.

[0137] Furthermore, according to the matching degree calculated in step 1 and the urgency adjustment coefficient μ l , the system automatically allocates the optimal resource combination for each subtask. The present invention uses the following formula for each subtask T(l) k Select the best resources:

[0138]

[0139] Among them, R(T(l) k ) represents subtask T(l) k The collection of resources to which it is allocated. Represents resource R i With subtask T(l) k The matching degree is directly inherited from the output of step 1. l It represents the urgency weight of task T(l), and higher urgency will give priority to resource allocation.

[0140] Example: For subtask T(1)1 (on-site first aid) of task T(1), the present invention allocates the following resources based on the maximum matching degree and urgency adjustment:

[0141] R(T(1)1)={V(2),P(4)}, i.e., the ambulance V(2) and the medical staff P(4).

[0142] Furthermore, to ensure reasonable resource allocation and avoid resource conflicts or duplication, the system needs to follow the following rules:

[0143] Resource uniqueness: Each resource can be assigned to only one task or subtask, unless the resource can be used simultaneously.

[0144] Load Check: The system automatically checks the current load of resources (such as whether vehicles are idle or the workload of medical staff) to avoid over-allocation of resources. If a resource is overloaded, the system will lower its priority by using the load penalty item in the matching degree.

[0145] Prioritize task urgency: When resources conflict, the system will prioritize the needs of urgent tasks, and lower-priority tasks may be delayed.

[0146] Furthermore, after the work order is generated, the system adjusts the coefficient μ according to the urgency of the task l and matching Sort all tasks to ensure that the most urgent tasks are responded to first. The priority of each task is calculated as:

[0147]

[0148] in, μ represents the matching degree between tasks and resources obtained from step 1. l represents the urgency adjustment factor of task T(l). Tasks with higher priority will be allocated resources first to ensure that tasks are processed efficiently according to their urgency.

[0149] Furthermore, the final output of the work order generation is a detailed task-resource allocation table, including each task's subtasks, allocated resources, and the priority order of each task. The work order output structure is:

[0150] Task T(l):

[0151] Task type, urgency l , geographical location.

[0152] Subtask T(l) k :

[0153] Detailed description of each subtask, such as first aid scene processing, patient transfer, etc.

[0154] The corresponding allocated resources R(T(l) k ), such as ambulance V(i), medical staff P(j), and equipment E(k).

[0155] Task prioritization:

[0156] According to the task priority calculated by the priority formula, tasks will be processed in descending order.

[0157] Example ticket:

[0158] Task T(1): Cardiac arrest, urgency μ1=0.95

[0159] Subtask T(1)1: On-site first aid

[0160] Allocation of resources: ambulance V (2), first aid personnel P (4)

[0161] Subtask T(1)2: Patient transfer

[0162] Allocate resources: Ambulance V(3)

[0163] Task T(2): Traffic accident, urgency μ2 = 0.85

[0164] Subtask T(2)1: First aid at the accident scene

[0165] Allocation of resources: Ambulance V (5), First Aid Personnel P (7)

[0166] Step 3: In response to the ever-changing emergency scenarios, a multi-agent-based optimization strategy is set up to coordinate the allocation and control of each resource and optimize task allocation.

[0167] Specifically, in the context of an emergency response platform, resources (such as ambulances, medical personnel, and equipment) must constantly respond to changing emergency tasks, and the demands of each task can change dynamically over time and with resource availability. Therefore, traditional multi-agent scheduling solutions often fail to handle the sudden nature of tasks, resource allocation conflicts, and dynamic task priorities.

[0168] To this end, the present invention has made the following targeted designs:

[0169] Input data structure:

[0170] Work order T(l): Work order information is passed from step 2, including the task urgency μ l , task type, geographical location, initial resource allocation R(T(l) k ).

[0171] Match Task T(l) and resource R i The initial matching degree.

[0172] Dynamic state of resources: Not only the static attributes of resources (skills, location, etc.) are considered, but also dynamic loads (such as the current number of tasks of ambulances, fatigue of medical staff, etc.) are introduced, which are not fully considered in steps 1 and 2.

[0173] Agent definition:

[0174] Vehicle Agent V(i): Each ambulance acts as an independent agent and can make decisions based on task matching and vehicle status.

[0175] Medical staff agent P(j): Each medical staff acts as an agent and selects tasks based on skill matching and load conditions.

[0176] Equipment agent E(k): Each emergency equipment acts as an agent to respond to mission requirements.

[0177] The states of these agents not only rely on the preliminary allocation data, but also take into account the dynamic changes of each resource, ensuring that the agents can optimize their decisions based on the latest conditions.

[0178] Specifically, in order to better match the real-time scheduling requirements of the emergency platform, the present invention specially designs the state space and reward mechanism of the intelligent agent to address the urgency and complexity of task scheduling.

[0179]

[0180] Among them, L i Represents resource R i The current load of U. This state variable updates the resource usage in real time and helps the agent decide whether to participate in new tasks. i Represents resource R i The availability of μ indicates whether the resource is idle. l It represents the urgency adjustment coefficient of task T(l), which is particularly important in emergency mission scenarios and directly affects the priority of resource scheduling. pos (T(l),R i ) represents the task T(l) and the resource R i Considering the time sensitivity in emergency scenarios, resources that are farther away will have lower priority. Indicates that the resource R i Collaboration status with other resources. For example, an ambulance and medical personnel may need to work together to complete a task.

[0181] Specifically, in order to enable the multi-agent system to better serve the allocation of emergency tasks, the present invention adds some special terms to the reward function, especially adjusting it for urgency and collaborative tasks.

[0182]

[0183] Among them, α·μ l Indicates the emphasis on the urgency of the task. α controls the priority of urgent tasks. In emergency scenarios, the present invention must ensure that high-urgency tasks can obtain resources first. Represents the matching weight between resources and tasks, and is used to reward resource agents that can complete tasks efficiently. collab (R i ) represents the collaborative work reward, which is a positive feedback specifically designed to consider cooperation between resources. For example, when a task requires the collaboration of an ambulance and equipment, agents V(i) and E(k) will receive additional rewards to encourage them to jointly respond to the task. i ) represents the resource load penalty item, which prevents resources from being overused or involved in tasks for a long time. It is suitable for fatigue management of medical staff and equipment life management, etc.

[0184] Specifically, in emergency scenarios, tasks often require the coordinated completion of multiple resources, such as medical staff, emergency equipment, and ambulances working together to complete the same task. To this end, the present invention designs a collaborative mechanism that optimizes task allocation among resources through game and negotiation mechanisms.

[0185] CollaborativeReward(R i ,R j )=C collab (R i ,R j )=λ·Efficiency(R i ,R j )-θ·D pos (R i ,R j )

[0186] Among them, λ represents the weight of collaborative efficiency, which indicates the efficiency of cooperation between resources. For example, when ambulances and medical staff work together, they can complete tasks faster. θ represents the distance penalty term. If the distance between two resources is far, the collaborative efficiency will be reduced. i ,R j ) indicates resource R i and R j The efficiency of cooperation on tasks is calculated using historical data.

[0187] Specifically, task conflicts and resource competition are common challenges in multi-agent systems. This is especially true in emergency response platforms, where different tasks have varying urgency but limited resources. This paper designs a resource conflict resolution mechanism to ensure maximum resource allocation efficiency.

[0188]

[0189] in, and Represents resource A i and A j With task T(l) k The matching degree of μ l Represents the urgency adjustment coefficient of task T(l).

[0190] Specifically, after multiple rounds of agent learning and collaborative game, all resource allocation plans will eventually converge to the optimal state and output the following:

[0191] The final resources allocated to each task T(l), including vehicles, medical staff, equipment, etc., are accompanied by the urgency of the task and the coordination of resources.

[0192] Specific resource execution paths ensure that tasks can be completed according to priority and time sensitivity.

[0193] Step 4: Monitor the optimized task allocation in real time, and combine it with the real-time data stream of real-time monitoring to ensure that the dynamic environment changes can be responded to at any time during the task execution process.

[0194] Specifically, the input of step 4 must be the output from step 3 and combined with real-time event stream data to ensure that the task execution process can respond to dynamic environmental changes at any time.

[0195] Furthermore, according to the output of step 3:

[0196] Task allocation table T(l): including task priority μ l , resource allocation R(T(l)), path planning P(T(l)), etc.

[0197] Resource Status S i : Dynamic load and availability status of resources generated by intelligent agent systems.

[0198] New real-time data:

[0199] Real-time event stream E(t): includes real-time changing data such as traffic, equipment status, and personnel location.

[0200] Furthermore, in order to dynamically adjust work orders to adapt to real-time changes in emergency scenarios, the present invention designs an event stream processing mechanism based on innovative application of information technology, which associates event streams with task work orders, allowing the system to automatically adjust task execution and resource scheduling in emergencies.

[0201] Furthermore, the present invention models the real-time event stream E(t) as a time series, which represents multiple events occurring in different time periods.

[0202] E(t)={e1(t),e2(t),...,e n (t)}

[0203] Each event e i (t) contains its metadata (event type, location, timestamp, etc.), which will affect different aspects of task execution.

[0204] Furthermore, each event e i (t) has different effects on task T(l). This paper designs an impact function to measure the impact of events on task priority, path, resource allocation, etc.:

[0205] ΔT l (e i (t))=w i ·δ(T l ,e i (t))

[0206] Among them, w i Indicates event e i The weight of (t) depends on the event type and task requirements (for example, traffic congestion has a greater impact on the path, while equipment failure has a greater impact on resource allocation). l ,e i (t)) represents the event e i (t) The specific impact on task T(l) (such as delay, equipment replacement, etc.).

[0207] Furthermore, the system will dynamically adjust the priority of tasks based on the impact of real-time events on tasks. The present invention introduces a priority adjustment formula based on the application of information technology innovation to ensure that task priorities can be flexibly changed according to external events.

[0208]

[0209] Among them, μ′ l Indicates the task priority adjusted after real-time event stream processing. l represents the original task priority output from step 3. l (e i (t)) represents each event e i (t) The increment to the task priority.

[0210] Furthermore, according to the real-time event stream, the present invention designs a set of dynamic adjustment rules based on the application innovation of information technology to ensure that task execution and resource allocation are always optimized.

[0211] Specifically, when traffic conditions change (such as traffic jam or road closure), the system will use event E traffic (t) Recalculate the path to ensure that resources can arrive at the task location on time. Path adjustment formula:

[0212] P′(T(l))=P(T(l))+ΔD(E traffic (t))

[0213] Where P(T(l)) represents the original path of task T(l). ΔD(E traffic (t)) represents the traffic event E traffic (t) is the path or time increment triggered. P′(T(l)) represents the optimal path after dynamic adjustment.

[0214] When the device status (such as device failure) event E device When (t) occurs, the system will dynamically replace the affected device. Device replacement formula:

[0215] R′(T(l) k )=R(T(l) k )-E fault +E backup

[0216] Among them, R(T(l) k ) represents the current subtask T(l) k Resource allocation. fault Indicates a faulty device. E backup Represents a standby device that automatically replaces the affected resource.

[0217] In emergency scenarios, patients may be transferred to different locations, and the system needs to quickly reallocate the nearest resources. The resource reallocation formula is as follows:

[0218]

[0219] in, Represents task T(l) and resource R i The matching degree. D pos (T(l),R i ) represents the new location of task T(l) and resource R i R′(T(l)) represents the resource configuration after dynamic adjustment.

[0220] Furthermore, in order to ensure that the system can quickly respond to the triggering of each event, the present invention designs an association mechanism between events and tasks to evaluate the actual impact of events on tasks and trigger corresponding adjustments.

[0221] Furthermore, the present invention uses an event impact measurement formula to measure the overall impact of each event on the task:

[0222]

[0223] Among them, I(T(l),E(t)) represents the overall impact index of event E(t) on task T(l). i represents the weight, based on the urgency of the event and the relevance of the task. i (t)) represents the specific impact of the event on the task.

[0224] When the event impact index exceeds a certain threshold, the system will automatically trigger the adjustment mechanism.

[0225] Output: Dynamically adjusted work order

[0226] Furthermore, the work order after dynamic event stream processing will include the following:

[0227] Task priority update: Task priorities are updated in real time based on the impact of events on tasks.

[0228] Resource allocation adjustment: Dynamically replace or adjust the resource configuration of tasks to ensure optimal resource utilization.

[0229] Path adjustment: Dynamically adjust path planning based on traffic changes to reduce task delays.

[0230] Step 5: Re-enter the optimized task allocation and resource scheduling strategies into the knowledge graph for closed-loop optimization, including:

[0231] Build a feedback model, take task execution feedback as input, and adjust future work order scheduling strategies in real time by comparing the actual task execution with the plan.

[0232] Specifically, the input of step 5 comes from the output of step 4, and combined with the feedback data during the task execution process, the system will use this data to form a closed-loop feedback loop and continuously optimize the execution effect of the work order. The input data includes:

[0233] Dynamically adjusted work order T′(l): Task priority μ′ generated after real-time event processing l , path planning P′(T(l)), resource allocation R′(T(l)) and other data.

[0234] Execution feedback F(t): Feedback information collected during task execution, including task completion time, task delay, resource utilization efficiency, task response time, etc.

[0235] Furthermore, to optimize the performance of work order execution, the system forms a closed-loop feedback system based on task execution feedback, continuously adjusting and improving task allocation and resource scheduling strategies. This paper introduces a feedback model, using task execution feedback F(t) as the core input for optimizing work order execution performance. The core of the closed-loop feedback model is to adjust future work order scheduling strategies in real time by comparing the actual execution of tasks with the plan. Feedback model formula:

[0236] ΔT′(l)=λ·(P actual (T(l))-P′(T(l)))+γ·F(t)

[0237] Where ΔT′(l) represents the improvement in task execution performance, which is used to adjust future task orders. λ represents the path optimization coefficient, which weighs the difference between the planned path and the actual path. actual (T(l)) represents the actual execution path of the task. P′(T(l)) represents the optimized planned path, output from step 4. γ represents the feedback weight coefficient, which measures the impact of feedback data on task adjustment. F(t) represents execution feedback data, including task response time, device status, etc.

[0238] Furthermore, by comparing the actual execution path P of the task actual (T(l)) and the planned path P′(T(l)), the system can continuously optimize the path selection for future tasks. After each task is executed, the system adjusts the path planning strategy based on the deviation to ensure a more accurate path for the next task assignment. The core formula for path optimization is:

[0239] P″(T(l))=P′(T(l))+ΔP(T(l))

[0240] Where P″(T(l)) represents the optimized path for the next execution. ΔP(T(l)) represents the path deviation, which corrects the planned path based on execution feedback. The path deviation ΔP(T(l)) is calculated from path feedback data, including differences in road congestion, real-time traffic data, and resource response times.

[0241] Furthermore, the closed-loop feedback system optimizes future resource allocation strategies by analyzing the resource utilization efficiency of each task. Based on feedback data, such as resource utilization and task execution time, the system adjusts resource allocation weights to ensure that efficient resources are prioritized for urgent tasks. Resource allocation optimization formula:

[0242] R″(T(l))=R′(T(l))+δ·F(t)

[0243] Here, R″(T(l)) represents the resource allocation result for the next task. δ represents the resource allocation weight coefficient. F(t) represents resource usage feedback, including device status and resource efficiency during task execution. Through this formula, the system can dynamically adjust the resource allocation strategy to make resources more efficiently utilized in future tasks and avoid repeated use of overloaded resources.

[0244] Furthermore, based on closed-loop feedback, the system can dynamically adjust task priorities, especially when resources are tight or tasks are delayed. Priority adjustment not only considers the original urgency of the task, but also l , and also combines the actual feedback data of task execution to ensure more accurate priority allocation. Priority optimization formula:

[0245] μ″ l =μ′ l +θ·F(t)

[0246] Where μ″ l represents the optimized task priority, used for the next round of task scheduling. θ represents the priority adjustment coefficient, which adjusts the priority based on task feedback data. F(t) represents task execution feedback data, including information such as task delays and resource conflicts. This optimization formula ensures that the system can dynamically respond to unexpected issues during task execution, preventing subsequent tasks from being delayed again due to the same issues.

[0247] Furthermore, after closed-loop feedback optimization, the system will generate an optimized work order for the next task scheduling, including:

[0248] Optimized task priority μ″ l : Dynamic adjustment based on feedback ensures more accurate priority allocation.

[0249] Optimized resource allocation R″(T(l)): Resource allocation adjusted by feedback data to ensure efficient resource utilization.

[0250] Optimized path planning P″(T(l)): A more accurate path planning for future task execution after optimization based on the actual execution path data.

[0251] In summary, this invention provides a more intelligent and dynamic solution that can better integrate multi-source information, automatically allocate and coordinate resources, and respond to complex emergency mission requirements in real time, thereby improving the overall efficiency and responsiveness of the emergency system. This requires not only the introduction of more advanced technical tools to manage complex resource networks, but also the development of algorithms capable of real-time optimization and dynamic adjustment to address the inherent shortcomings of traditional systems.

[0252] These are merely some preferred embodiments of the present invention, and certainly cannot be used to limit the scope of rights of the present invention. A person skilled in the art can understand that all or part of the processes of the above embodiments and equivalent changes made in accordance with the claims of the present invention still fall within the scope of the invention.

Claims

1. A work order management method for an emergency platform based on digital twins and artificial intelligence, characterized by: The method comprises the following steps: Step 1: Collect historical and real-time emergency data from emergency scenarios to build a knowledge graph, and then build a task scheduling system based on the knowledge graph, including: S11. Obtain historical emergency data and real-time data to form total data; S12. Treat the emergency resources and tasks in the total data as nodes in the knowledge graph. The nodes are connected by relationship edges to represent the potential matching relationship between resources and tasks. Each relationship expresses the degree of association between nodes through a dynamic weight. S13. For each task, calculate the comprehensive matching degree between each task and various emergency resources to select the optimal resource allocation. At the same time, design an urgency adjustment coefficient for each task to ensure that high-urgency tasks are allocated resources first; S14. Calculate work order data including optimal resource allocation and optimal task priority; Step 2: Decompose the task into several subtasks based on the work order data and assign the optimal combination of emergency resources to each subtask, including: S21. Automatically generate work orders based on the reasoning and matching results of the knowledge graph; S22. Decompose each emergency task into several subtasks, and then allocate the optimal resource combination to each subtask based on the comprehensive matching degree and urgency adjustment coefficient; S23. After the work order for each subtask is generated, all tasks are sorted according to the comprehensive matching degree of the subtasks and the urgency adjustment coefficient to ensure that the most urgent tasks are responded to first, and the final work order is output; Step 3: For the ever-changing emergency scenarios, set up a multi-agent-based optimization strategy to coordinate the allocation and control of each resource and optimize task allocation, including: S31. Define multiple agents including: vehicle agent, medical staff agent, and equipment agent; S32. Design the state space and reward mechanism of multiple agents based on the real-time scheduling requirements of the emergency platform to conduct multi-intelligence control. At the same time, design a resource coordination mechanism to optimize task allocation through game and negotiation mechanisms between resources. Design a resource conflict resolution mechanism to address conflicts between emergency tasks and ensure maximum resource allocation efficiency. S33. After multiple rounds of agent learning and collaborative game, all resource allocation plans will eventually converge to the optimal state and output; Step 4: Monitor the optimized task allocation in real time. Combined with the real-time data stream of real-time monitoring, ensure that the dynamic environment changes can be responded to at any time during task execution, including: S41. Collect real-time data streams and convert them into time series for modeling. The real-time data streams contain multiple events that occur in different time periods. Different impact functions are designed for each event's impact on the task to measure the impact of the event. Then, the priority of the task is dynamically adjusted based on the impact of each event on the task. S42. Design dynamic adjustment rules based on real-time data streams to ensure that task execution and resource allocation are always optimized; S43. Design an event-task association mechanism to assess the actual impact of events on tasks and trigger corresponding adjustments. When the event impact index exceeds a certain threshold, the adjustment mechanism will be automatically triggered. S45. Output the optimized and adjusted work order; Step 5: Re-enter the optimized task allocation and resource scheduling strategies into the knowledge graph for closed-loop optimization, including: Build a feedback model, take task execution feedback as input, and adjust future work order scheduling strategies in real time by comparing the actual task execution with the plan.

2. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 1 is characterized in that: The structure of the knowledge graph includes: The nodes include: Vehicle Node Indicates the an ambulance; Medical staff node Indicates the medical staff; device node Indicates the First aid equipment; Task Node Indicates the A first aid mission; Relationships include: Define the relationship between various resources and tasks, using 、 、 Indicates the matching relationship between each resource and task.

3. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 2 is characterized in that: Said S13 specifically includes: Each task It needs to match with multiple resources and calculate the comprehensive matching degree between resources and tasks. To select the optimal resource allocation, it is expressed as follows: ； in, Indicates a task and resources degree of matching; Represents a resource With the task The matching degree of a specific attribute; Representation attributes The weights represent the importance of different attributes; represents the innovative real-time distance regularization term, and represents the resource With the task The difference in geographical distance, is the geographical distance between the task and the resource, is the regularization coefficient; represents the resource load penalty term, The current resource load level; The matching degree calculation for emergency tasks needs to take into account the urgency of the task. Therefore, a dynamic adjustment item is designed for each task to ensure that resources are allocated to high-urgency tasks first, as shown below: ； in, Indicates the Dynamic priority adjustment coefficient of each task; Indicates the The urgency of the task, The value is determined by the mission emergency assessment model; Indicates the sum of the urgency of all current tasks.

4. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 1, characterized in that: According to the comprehensive matching degree and urgency adjustment coefficient, the optimal resource combination is allocated to each subtask, which is expressed as follows: ； in, Represents a subtask the set of resources allocated; Represents a resource With subtasks degree of matching; Indicates a task The urgency weight of In S22, to ensure reasonable resource allocation and avoid resource conflicts or duplication, task allocation needs to follow the following rules: Each resource can be assigned to only one task or subtask, unless the resource can be used simultaneously; Automatically check the current load of resources to avoid over-allocation of resources. If a resource is overloaded, its priority will be lowered by using the load penalty item in the matching degree. When there is a resource conflict, the needs of urgent tasks are met first, and low-priority tasks are delayed.

5. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 4 is characterized in that: The final work order is a task-resource allocation table, including the subtasks of each task, the allocated resources, and the priority order of each task. The final work order structure is shown as follows: Task :Task type, urgency , geographical location; Subtasks : Detailed description of each subtask; corresponding allocated resources ; Task priority sorting: Tasks will be processed in descending order based on the task priority calculated by the priority formula.

6. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 1, characterized in that: The S32 specifically includes: The state space and reward mechanism of the intelligent agent are designed to address the urgency and complexity of task scheduling. The state space is represented as follows: ； in, Represents a resource The current load; Represents a resource Availability, indicating whether the resource is idle; Indicates a task The urgency adjustment factor; Indicates a task and resources geographical distance; Indicates that the resource Collaboration status with other resources; The reward mechanism is as follows: ； in, Expresses emphasis on the urgency of the task; Control the priority increase of urgent tasks; Represents the matching weight between resources and tasks, and is used to reward resource agents that can complete tasks efficiently; represents the collaborative work reward item, which is used to consider the positive feedback of cooperation between resources; Represents the resource load penalty term to prevent resources from being overused or participating in tasks for a long time; The collaboration mechanism is as follows: ； in, The weight of collaborative efficiency represents the efficiency of cooperation between resources; represents the distance penalty term. If the distance between two resources is far, the benefit of their collaboration will be reduced. Represents a resource and The efficiency of cooperation on tasks is calculated using historical data; The resource conflict resolution mechanism is as follows: ； in, and Represents resources and With the task degree of matching; Indicates a task The urgency adjustment factor.

7. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 6 is characterized in that: Eventually, all resource allocation plans converge to the optimal state and are output. The output includes: The final resources assigned to each task, along with task urgency and resource coordination, as well as specific resource execution paths, ensure that tasks can be completed according to priority and time sensitivity.

8. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 1, characterized in that: The dynamic adjustment of the task priority according to the impact of each event on the task is expressed as follows: ； in, Indicates the task priority adjusted after real-time event stream processing; Indicates the original task priority; Represents each event The increment to the task's priority.

9. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 1, characterized in that: The design of dynamic adjustment rules based on real-time data streams includes: When traffic conditions change, through events Recalculate the path to ensure that resources can arrive at the task location on time. The path adjustment formula is as follows: ； in, Indicates a task The original path; Indicates traffic incidents The path increment induced; Indicates the optimal path after dynamic adjustment; When the device status event When an error occurs, the affected device is dynamically replaced. The device replacement formula is as follows: ； in, Indicates the current subtask resource allocation; Indicates a malfunctioning device; Indicates a standby device that automatically replaces the affected resource; In emergency scenarios, patients may be transferred to different locations, requiring the nearest resources to be reallocated. The resource reallocation formula is as follows: ； in, Indicates a task and resources degree of matching; Indicates a task New location and resources geographical distance; Indicates the resource configuration after dynamic adjustment; The association mechanism between events and tasks is as follows: ； in, Representing an event Task Overall impact index; Represents weight, based on the urgency of the event and the relevance of the task; Indicates the specific impact of the event on the task.

10. The method for managing emergency platform work orders based on digital twins and artificial intelligence according to claim 1, characterized in that: The feedback model is expressed as follows: ； in, Indicates the improvement in task execution performance, used to adjust future task orders; It represents the path optimization coefficient, which weighs the difference between the planned path and the actual path; Indicates the actual execution path of the task; represents the optimized planned path; Represents the feedback weight coefficient, which measures the impact of feedback data on task adjustment; Indicates execution feedback data.

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