Rapid first-aid intelligent scheduling system
By designing a fast first aid intelligent scheduling system, the problem of unreasonable allocation of first aid resources in the existing technology has been solved, the rapid and reasonable allocation of medical resources has been achieved, and the first aid efficiency and resource utilization have been improved.
Patent Information
- Application Number
- CN202510157621.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-13
- Publication Date
- 2025-05-16
AI Technical Summary
The existing rapid first aid methods have problems with lengthy processes and low efficiency, and lack of rational allocation between the demand for help and medical equipment, resulting in the inability to use the best use of first aid medical resources, resulting in waste of medical resources and delayed treatment time.
A fast first aid intelligent scheduling system was designed. By updating the medical equipment data of each first aid site in real time and obtaining help information, calculating the first aid capabilities of each first aid site and prioritizing the allocation, establishing a multi-objective optimization model for intelligent scheduling of emergency medical equipment for emergency events, and building a target optimization neural network model to generate a fast first aid intelligent scheduling solution.
It has achieved rapid and reasonable allocation of medical resources, improved first aid efficiency, maximized resource utilization, and reduced waste of medical resources and delayed treatment time.
Smart Images
Figure CN120013191A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of emergency dispatching, and in particular to a rapid emergency intelligent dispatching system. Background Art
[0002] With the rapid development of medical information construction in my country, medical institutions at all levels have improved the efficiency of the medical process through information and networking construction and application, and have achieved remarkable results in process improvement. In the field of emergency medical care, due to the continuous deepening of information application, the various links of emergency care have initially achieved process connection, data sharing, and coordinated treatment by medical institutions at all levels. The overall capacity of critical care emergency care has been significantly improved, which has played a vital role in improving the efficiency of the use of emergency medical resources.
[0003] At present, there are still widespread problems of lengthy processes and low efficiency in the field of emergency medical services, which leads to a lack of rational allocation between medical needs and medical resources in the implementation of emergency treatment when emergency incidents occur, resulting in the inability to make optimal use of emergency medical resources, causing waste of medical resources, delaying treatment time, and affecting the timeliness, fairness and rationality of emergency medical services.
[0004] At the same time, the existing rapid first aid methods have problems such as lengthy processes and low efficiency. There is also a lack of rational allocation between the demand for help and medical equipment, which makes it impossible to make optimal use of emergency medical resources, resulting in waste of medical resources and delayed treatment time. There is also a lack of a reasonable and effective rapid first aid intelligent scheduling plan. Summary of the invention
[0005] The present invention provides a rapid emergency intelligent dispatching system to solve the problems of lengthy procedures and low efficiency in existing rapid emergency means, as well as the lack of rational configuration between the demand for help and medical equipment, which makes it impossible to make optimal use of emergency medical resources, resulting in waste of medical resources and delay in treatment time, and the lack of a reasonable and effective rapid emergency intelligent dispatching plan.
[0006] A rapid emergency intelligent dispatching method comprises the following steps: S1. Update the medical equipment data of each emergency station in real time and obtain help information; form emergency tasks based on the help information, and calculate the emergency capabilities of each emergency station based on the emergency tasks; divide the deployment priority according to the emergency capabilities of each emergency station for the emergency tasks; S2. Establish a multi-objective optimization model for the intelligent dispatch of emergency medical equipment, and construct a target optimization neural network model to generate a rapid emergency intelligent dispatch plan.
[0007] Preferably, the S1 specifically includes: Based on the current emergency task, each emergency station is task analyzed to obtain the emergency capabilities of the emergency station related to the current emergency task; the emergency capability is the rescue resources that each emergency station can deploy for the current emergency task, as well as the historical emergency capability of each emergency station for the current emergency task type; the specific calculation process of the emergency capability is: ; in, Indicates The first aid capabilities of each first aid station for the current first aid mission; Indicates the first Rescue resources, , Indicates the number of rescue resource types; Indicates Quality rating of rescue resources; Indicates the resource aging factor; Indicates The emergency response speed of each first aid station; rescue resources are mainly medical equipment, Indicates The average historical emergency capability of each emergency station for the current emergency mission type; Indicates Historical performance factors of emergency sites.
[0008] Preferably, the S1 specifically includes: The calculation of the provisioning priority is: ; ; in, Indicates The priority value of each emergency station; Indicates The first aid capabilities of each first aid station for the current first aid mission, , Indicates the number of first aid sites; It is The priority adjustment weight of each emergency site; It is Traffic delay factor from the emergency station to the incident site; Is the weather condition The impact of emergency response time on It is Current workload of emergency response sites; , , are the weight factors for traffic, weather, and workload, respectively; According to the established priority value threshold, the priority value of each emergency site is divided into a corresponding priority interval, and the emergency sites with the same priority are grouped into priority groups.
[0009] Preferably, the S2 specifically includes: The parameters of the multi-objective optimization model are set, including first aid sites, types of rescue resources, medical emergencies, the number of rescue resources required for first aid tasks, the number of rescue resources allocated by each first aid site for first aid tasks, the number of remaining rescue resources at each first aid site, the time required from the first aid site to the site of the medical emergency, and the priority range; each medical emergency corresponds to a first aid task.
[0010] Preferably, the S2 specifically includes: Based on the multi-objective optimization model parameters, the objective function and constraints of rapid emergency intelligent dispatch are constructed. The specific formula is: ; ; ; ; ; ; in, represents the objective function of rapid emergency intelligent dispatch; It indicates the number of medical emergencies occurring simultaneously; Indicates the serial number of any medical emergency; Indicates First aid station to Time required for each medical emergency site; Indicates First aid stations for The number of rescue resources deployed for each emergency mission; Indicates The number of rescue resources required for each emergency mission; Indicates The number of remaining rescue resources at each emergency station; Indicates The first aid station is Provide transportation efficiency of rescue resources for emergency medical incidents; Indicates constraints; Indicates Priority groups; Indicates Priority groups; Represents a non-negative integer.
[0011] Preferably, the S2 specifically includes: A target optimization neural network model is constructed, and each dispatching strategy is used as a solution to the objective function of rapid emergency intelligent dispatching. Different dispatching actions are performed to form different dispatching strategies; the dispatching action is the action of delivering rescue resources from the emergency station to the site of the sudden medical event.
[0012] Preferably, the S2 specifically includes: The scheduling action is input into the target optimization neural network model; the target optimization neural network model constructs single-layer neurons layer by layer, trains a single-layer network each time, and inputs the output of each single-layer network into each neuron in the feature layer and the operation layer; the feature layer extracts features from the output of each single-layer network to obtain a feature set, and the feature layer outputs the feature set to the summation layer; the operation layer outputs the state of each neuron to the summation layer, and the summation layer outputs the result to the output layer; the output of the output layer is the optimal solution of the objective function of rapid emergency intelligent scheduling, thereby generating a rapid emergency intelligent scheduling plan.
[0013] Preferably, the S2 specifically includes: The state of each neuron in the working layer is expressed as: ; in, Indicates The output of a single-layer network; Indicates The output of a single layer network The state of the neuron; Represents the probability distribution of each single-layer network output; is the regularization term.
[0014] A rapid emergency intelligent dispatching system, comprising the following parts: Resource interface, event interface, alarm module, task receiving module, task analysis module, task division module, target construction module, intelligent optimization module and scheduling module; Resource interface, used to update the medical equipment data of each emergency site in real time; the resource interface is connected to the task analysis module and the target construction module by data transmission; The event interface is used to obtain help information in real time, and the event interface is connected to the alarm module in a data transmission manner; The alarm module is used to send alarm information to medical staff; The task receiving module is used to obtain the emergency task, and the task receiving module is connected to the task analysis module in a data transmission manner; The task analysis module is used to perform task analysis on each emergency station according to the current emergency task, and obtain the emergency capabilities of the emergency station related to the current emergency task; the task analysis module is connected to the task division module and the target construction module by data transmission; The task division module is used to divide the deployment priority of each emergency station based on the current emergency task according to the emergency capacity of each emergency station for the current emergency task; divide the priority value into corresponding priority intervals according to the established priority value threshold, and form the emergency stations of the same priority into a priority group; the task division module is connected to the target construction module in a data transmission manner; The target construction module establishes a multi-objective optimization model for intelligent scheduling of emergency medical equipment based on priority groups and emergency capabilities; constructs the objective function and constraint conditions for rapid emergency intelligent scheduling, and the target construction module is connected to the intelligent optimization module by data transmission; The intelligent optimization module constructs a target optimization neural network model, solves the objective function of rapid emergency intelligent scheduling, and obtains a rapid emergency intelligent scheduling plan. The intelligent optimization module is connected to the scheduling module in a data transmission manner; The scheduling module is used to form and display scheduling instructions.
[0015] The beneficial effects of the technical solution of the present invention are: 1. Combine multi-objective planning theory with the actual operation process of rapid emergency medical equipment dispatch, focus on maximizing dispatch time and providing medical equipment, comprehensively measure the specific emergency capabilities of emergency sites, and combine emergency mission requirements to comprehensively and accurately obtain rapid emergency intelligent dispatch solutions that can not only complete rescue missions quickly but also maximize resource utilization.
[0016] 2. Establish a multi-objective optimization model for the intelligent scheduling of emergency medical equipment, construct a target optimization neural network model, form different scheduling strategies based on scheduling actions, have excellent feature learning capabilities, and effectively overcome the difficulty of deep neural network training. Through this deep nonlinear network result, generate a rapid emergency intelligent scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 This is a structural diagram of a rapid emergency intelligent dispatching system according to the present invention; Figure 2 This is a flow chart of a rapid emergency intelligent dispatching method according to the present invention. DETAILED DESCRIPTION
[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
[0020] The specific scheme of a rapid emergency intelligent dispatching system provided by the present invention is described in detail below with reference to the accompanying drawings.
[0021] Refer to the attached Figure 1 , which shows a structure diagram of a rapid emergency intelligent dispatching system provided by an embodiment of the present invention, the system includes the following parts: Resource interface 10, event interface 20, alarm module 30, task receiving module 40, task analysis module 50, task division module 60, target construction module 70, intelligent optimization module 80 and scheduling module 90; The resource interface 10 is used to update the medical equipment data of each emergency site in real time. The medical equipment data includes the type, quantity, function and corresponding medical event type of the medical equipment at each emergency site. The resource interface 10 is connected to the task analysis module 50 and the target construction module 70 in a data transmission manner. The event interface 20 is used to obtain help information in real time. The event interface 20 is connected to the alarm module 30 in a data transmission manner; The alarm module 30 is used to send alarm information to medical staff; The task receiving module 40 is used to obtain the emergency task. The task receiving module 40 is connected to the task analyzing module 50 by data transmission; The task analysis module 50 is used to perform targeted task analysis on each first aid station according to the current first aid task, and obtain the first aid capability of the first aid station related to the current first aid task; the first aid capability is the rescue resources that each first aid station can deploy for the current first aid task, and the historical first aid capability of each first aid station for the current first aid task type, wherein the rescue resources are mainly medical equipment; the task analysis module 50 is connected to the task division module 60 and the target construction module 70 in the form of data transmission; The task division module 60 is used to divide the deployment priority of each emergency station based on the current emergency task according to the emergency capacity of each emergency station for the current emergency task; set up multiple priority value thresholds, divide the priority values into corresponding priority intervals according to the priority value thresholds, and form emergency stations with the same priority into a priority group; the task division module 60 is connected to the target construction module 70 in a data transmission manner; The target construction module 70 establishes a multi-objective optimization model for intelligent scheduling of emergency medical equipment based on priority groups and emergency capabilities; constructs the objective function and constraint conditions for rapid emergency intelligent scheduling, and the target construction module 70 is connected to the intelligent optimization module 80 in a data transmission manner; The intelligent optimization module 80 is used to construct a target optimization neural network model to solve the objective function of rapid emergency intelligent scheduling; each scheduling strategy is used as a solution of the objective function of rapid emergency intelligent scheduling, and the rapid emergency intelligent scheduling system performs different scheduling actions to form different scheduling strategies. The output of the target optimization neural network model is the optimal solution of the objective function, and the rapid emergency intelligent scheduling plan is obtained. The intelligent optimization module 80 is connected to the scheduling module 90 in a data transmission manner; The scheduling module 90 is used to form and display scheduling instructions.
[0022] Refer to the attached Figure 2 , which shows a flow chart of a rapid emergency intelligent dispatching method provided by an embodiment of the present invention, the method comprising the following steps: S1. Update the medical equipment data of each emergency station in real time and obtain help information; form emergency tasks based on the help information, and calculate the emergency capabilities of each emergency station based on the emergency tasks; divide the deployment priority according to the emergency capabilities of each emergency station for the emergency tasks; The rapid emergency intelligent dispatch system is used to intelligently dispatch the medical equipment of each emergency station to the corresponding event site according to the type of medical event when an emergency medical event occurs, so as to achieve rapid and reasonable allocation of medical equipment and improve emergency efficiency. The resource interface 10 updates the medical equipment data of each emergency station in real time. The medical equipment data includes the type, quantity, function and corresponding medical event type of the medical equipment at each emergency station. The event interface 20 obtains help information in real time.
[0023] When the rapid emergency intelligent dispatch system receives a help message, the alarm module 30 sends an alarm message to the medical staff, and the medical staff finds the patient's historical information from the big database, and the patient's historical information includes the patient's medical history and family history; the medical staff quickly determines the type of medical event based on the help message based on the patient's historical information, and determines the severity of the current medical event and the number of medical equipment that needs to be allocated, and forms an emergency task. The emergency task includes the type and quantity of medical equipment required for the current emergency medical event and the location of the event site. The medical staff inputs the emergency task into the rapid emergency intelligent dispatch system.
[0024] The task receiving module 40 obtains the first aid task and transmits the first aid task to the task analysis module 50. The task analysis module 50 performs targeted task analysis on each first aid station based on the current first aid task to obtain the first aid capability of the first aid station related to the current first aid task; the first aid capability is the rescue resources that each first aid station can deploy for the current first aid task, and the historical first aid capability of each first aid station for the current first aid task type; the calculation of the first aid capability mainly depends on the amount of resources required for the current first aid task and the average of the historical first aid capability, and also introduces the quality of the rescue resources (quality score), the emergency response speed of the first aid station, the resource aging factor, etc., to more comprehensively evaluate the first aid capability. The calculation process of the first aid capability is: ; in, Indicates The first aid capabilities of each first aid station for the current first aid mission; Indicates the first Rescue resources, , Indicates the number of rescue resource types; Indicates The quality score of the rescue resources ranges from [0,1] and is obtained by manually evaluating the service life, failure rate, performance indicators, etc. of the medical equipment; It represents the resource aging factor, which increases with the number of times the rescue resources are used; Indicates The emergency response speed of each first aid station is used to reflect the overall efficiency of the first aid station. In the present invention, rescue resources are mainly medical equipment. Indicates The average historical emergency capability of each emergency station for the current emergency mission type; Indicates Historical performance factors of emergency sites.
[0025] The task division module 60 divides the deployment priority of each emergency station based on the current emergency task according to the emergency capability of each emergency station for the current emergency task. The deployment priority is calculated as: ; ; in, Indicates The priority value of the emergency station, Indicates The first aid capabilities of each first aid station for the current first aid mission, , Indicates the number of first aid sites; It is The priority adjustment weight of the emergency sites, It is The traffic delay coefficient from the emergency station to the incident site reflects the impact of the current traffic conditions on the response speed; Is the weather condition The impact of emergency response time on It is The current workload of the emergency station is measured by the work saturation of the emergency station, ranging from [0,1], , , They are the weight factors of traffic, weather and workload respectively. According to the expert experience method, multiple priority value thresholds are set up. According to the priority value thresholds, the priority values are divided into corresponding priority intervals, and the emergency sites with the same priority are grouped into a priority group.
[0026] S2. Establish a multi-objective optimization model for the intelligent dispatch of emergency medical equipment, and construct a target optimization neural network model to generate a rapid emergency intelligent dispatch plan.
[0027] Establish a multi-objective optimization model for the intelligent dispatch of emergency medical equipment, and generate a rapid emergency intelligent dispatch plan from the emergency station to the incident site based on the emergency task. The rapid emergency intelligent dispatch system needs to complete the intelligent dispatch of rescue resources from the emergency station to the incident site, and set the parameters of the multi-objective optimization model, specifically: set the existence First aid stations, ,use Indicates the serial number of any emergency station; sets the rescue resource type kind, ,use Display the sequence number of any rescue resource; set to occur simultaneously A medical emergency, ,use Indicates the serial number of any medical emergency. Each medical emergency corresponds to an emergency task. The number of rescue resources required for a first aid mission is ; Set the number of rescue resources allocated to each emergency station for emergency tasks to ; Set the The number of remaining rescue resources at the emergency stations is ; Set the First aid station to The time required for a medical emergency site is ; Set a shared priority intervals, using Indicates The first aid station belongs to Priority groups, .
[0028] The target construction module 70 constructs the target function and constraint conditions of rapid emergency intelligent dispatching, and the specific formula is: ; ; ; ; ; ; in, represents the objective function of rapid emergency intelligent dispatch; Indicates First aid stations for The number of rescue resources deployed for each emergency mission; Indicates The first aid station is The transportation efficiency of rescue resources for each medical emergency is considered, taking into account factors such as resource scheduling and loading and unloading time; Indicates Priority groups; Indicates Priority groups; represents the constraints, Represents a non-negative integer.
[0029] Due to the diversity of the objective functions of the multi-objective optimization model for intelligent scheduling of emergency medical equipment, the concept of the optimal solution is complicated. Therefore, a deep learning algorithm is used to solve the objective function of rapid emergency intelligent scheduling. The intelligent optimization module 80 constructs a target optimization neural network model and randomly initializes the weights of the target optimization neural network model. Each scheduling strategy is used as a solution to the objective function of rapid emergency intelligent scheduling. The rapid emergency intelligent scheduling system performs different scheduling actions to form different scheduling strategies. The scheduling action is the action of the emergency station to transport rescue resources to the site of the emergency medical event. The scheduling action is represented by a data tuple: .
[0030] Schedule action Input into the target optimization neural network model, which adopts a multi-hidden layer perception architecture. First, single-layer neurons are constructed layer by layer, and a single-layer network is trained each time. The specific formula is: ; in, Indicates The output of a single-layer network; represents the activation function; Indicates The weights of a single-layer network; Indicates The bias of a single-layer network.
[0031] When all layers are trained, the output of each single-layer network is input into each neuron of the feature layer and the operation layer. The feature layer extracts features from the output of each single-layer network to obtain a feature set. , the feature layer outputs the feature set to the summation layer; The state of each neuron in the working layer is expressed as: ; in, express The state of the neuron; Represents the probability distribution of each single-layer network output; is a regularization term. The operating layer outputs the states of each neuron to the summation layer, and the calculation of the summation layer is: ; in, is a conditional function, indicating In the conditions The summation layer outputs the result to the output layer, and the calculation of the output layer is: ; in, Represents the output of the output layer; represents the weight of the output layer; Represents the bias of the output layer. The goal is to optimize the output of the neural network model That is, the optimal solution of the objective function of rapid emergency intelligent dispatching is obtained, thereby obtaining a rapid emergency intelligent dispatching plan. The rapid emergency intelligent dispatching plan is transmitted to the dispatching module 90, and the dispatching module 90 forms a dispatching instruction for display.
[0032] To sum up, a rapid emergency intelligent dispatch system described in this application is completed.
[0033] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0034] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
[0035] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.
Claims
1. A rapid emergency intelligent dispatching method, characterized in that: The following steps are involved: S1. Update the medical equipment data of each emergency station in real time and obtain help information; form emergency tasks based on the help information, and calculate the emergency capabilities of each emergency station based on the emergency tasks; divide the deployment priority according to the emergency capabilities of each emergency station for the emergency tasks; S2. Establish a multi-objective optimization model for the intelligent dispatch of emergency medical equipment, and construct a target optimization neural network model to generate a rapid emergency intelligent dispatch plan.
2. The rapid emergency intelligent dispatching method according to claim 1 is characterized in that: The S1 specifically includes: Based on the current emergency task, each emergency station is task analyzed to obtain the emergency capabilities of the emergency station related to the current emergency task; the emergency capability is the rescue resources that each emergency station can deploy for the current emergency task, as well as the historical emergency capability of each emergency station for the current emergency task type; the specific calculation process of the emergency capability is: ； in, Indicates The first aid capabilities of each first aid station for the current first aid mission; Indicates the first Rescue resources, , Indicates the number of rescue resource types; Indicates Quality rating of rescue resources; Indicates the resource aging factor; Indicates The emergency response speed of each first aid station; rescue resources are mainly medical equipment, Indicates The average historical emergency capability of each emergency station for the current emergency mission type; Indicates Historical performance factors of emergency sites.
3. The rapid emergency intelligent dispatching method according to claim 2 is characterized in that: The S1 specifically includes: The calculation of the provisioning priority is: ； ； in, Indicates The priority value of each emergency station; Indicates The first aid capabilities of each first aid station for the current first aid mission, , Indicates the number of first aid sites; It is The priority adjustment weight of each emergency site; It is Traffic delay factor from the emergency station to the incident site; Is the weather condition The impact of emergency response time on It is Current workload of emergency sites; , , are the weight factors for traffic, weather, and workload, respectively; According to the established priority value threshold, the priority value of each emergency site is divided into a corresponding priority interval, and the emergency sites with the same priority are grouped into priority groups.
4. The rapid emergency intelligent dispatching method according to claim 1 is characterized in that: The S2 specifically includes: The parameters of the multi-objective optimization model are set, including first aid sites, types of rescue resources, medical emergencies, the number of rescue resources required for first aid tasks, the number of rescue resources allocated by each first aid site for first aid tasks, the number of remaining rescue resources at each first aid site, the time required from the first aid site to the site of the medical emergency, and the priority range; each medical emergency corresponds to a first aid task.
5. The rapid emergency intelligent dispatching method according to claim 4 is characterized in that: The S2 specifically includes: Based on the multi-objective optimization model parameters, the objective function and constraints of rapid emergency intelligent dispatch are constructed. The specific formula is: ； ； ； ； ； ； in, represents the objective function of rapid emergency intelligent dispatch; indicates the number of medical emergencies occurring simultaneously; Indicates the serial number of any medical emergency; Indicates First aid station to Time required for each medical emergency site; Indicates First aid stations for The number of rescue resources deployed for each emergency mission; Indicates The number of rescue resources required for each emergency mission; Indicates The number of remaining rescue resources at each emergency station; Indicates The first aid station is Provide transportation efficiency of rescue resources for emergency medical incidents; Indicates constraints; Indicates Priority groups; Indicates Priority groups; Represents a non-negative integer.
6. The rapid emergency intelligent dispatching method according to claim 5 is characterized in that: The S2 specifically includes: A target optimization neural network model is constructed, and each dispatching strategy is used as a solution to the objective function of rapid emergency intelligent dispatching. Different dispatching actions are performed to form different dispatching strategies; the dispatching action is the action of delivering rescue resources from the emergency station to the site of the sudden medical event.
7. The rapid emergency intelligent dispatching method according to claim 6, characterized in that: The S2 specifically includes: The scheduling action is input into the target optimization neural network model; the target optimization neural network model constructs single-layer neurons layer by layer, trains a single-layer network each time, and inputs the output of each single-layer network into each neuron in the feature layer and the operation layer; the feature layer extracts features from the output of each single-layer network to obtain a feature set, and the feature layer outputs the feature set to the summation layer; the operation layer outputs the state of each neuron to the summation layer, and the summation layer outputs the result to the output layer; the output of the output layer is the optimal solution of the objective function of rapid emergency intelligent scheduling, thereby generating a rapid emergency intelligent scheduling plan.
8. The rapid emergency intelligent dispatching method according to claim 7 is characterized in that: The S2 specifically includes: The state of each neuron in the working layer is expressed as: ； in, Indicates The output of a single-layer network; Indicates The output of a single layer network The state of the neuron; Represents the probability distribution of each single-layer network output; is the regularization term.
9. A rapid emergency intelligent dispatching system, applied to the rapid emergency intelligent dispatching method according to claim 1, characterized in that: Includes the following sections: Resource interface, event interface, alarm module, task receiving module, task analysis module, task division module, target construction module, intelligent optimization module and scheduling module; Resource interface, used to update the medical equipment data of each emergency site in real time; the resource interface is connected to the task analysis module and the target construction module by data transmission; The event interface is used to obtain help information in real time, and the event interface is connected to the alarm module in the form of data transmission; The alarm module is used to send alarm information to medical staff; The task receiving module is used to obtain the emergency task, and the task receiving module is connected to the task analysis module in a data transmission manner; The task analysis module is used to perform task analysis on each emergency station according to the current emergency task, and obtain the emergency capabilities of the emergency station related to the current emergency task; the task analysis module is connected to the task division module and the target construction module by data transmission; The task division module is used to divide the deployment priority of each emergency station based on the current emergency task according to the emergency capacity of each emergency station for the current emergency task; divide the priority value into corresponding priority intervals according to the established priority value threshold, and form the emergency stations of the same priority into a priority group; Task The partitioning module is connected to the target building module by data transmission; The target construction module establishes a multi-objective optimization model for intelligent scheduling of emergency medical equipment based on priority groups and emergency capabilities; constructs the objective function and constraint conditions for rapid emergency intelligent scheduling, and the target construction module is connected to the intelligent optimization module by data transmission; The intelligent optimization module constructs a target optimization neural network model, solves the objective function of rapid emergency intelligent scheduling, and obtains a rapid emergency intelligent scheduling plan. The intelligent optimization module is connected to the scheduling module in a data transmission manner; The scheduling module is used to form and display scheduling instructions.