Wounded transferring method, device, equipment, medium and program product

Abstract

The invention provides a wounded transferring method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring current disaster information corresponding to a current disaster; determining simulated wounded information corresponding to the current disaster information and the treatment capability of a treatment site; based on the simulated wounded person information and the treatment capability of the treatment site, generating a wounded person transfer strategy; wherein the number of the wounded transferring strategies is at least one; simulating each wounded person transfer strategy to obtain a transfer index of each wounded person transfer strategy; and determining a target wounded person transfer strategy according to the transfer index of each wounded person transfer strategy so as to transfer the wounded person based on the target wounded person transfer strategy. By adopting the method provided by the embodiment of the invention, the transfer efficiency and effect can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a method, device, equipment, medium and program product for transporting a wounded person. Background Art

[0002] In related technologies, when responding to mass casualties, the purpose of medical rescue response is to use limited medical resources to treat as many wounded and sick as possible, and to maximize the efficiency of treatment. After the wounded and sick are determined to be at the level of diversion, they need to be transferred to a suitable medical institution for comprehensive medical treatment. This process involves the selection and decision-making of transportation tools and the location of the medical institution for evacuation. At present, at the site of medium- and large-scale disasters, the decision-making for the transfer of batches of wounded often relies on the personal experience of the on-site commanders. This method lacks scientific analysis and quantitative basis, and different people may make different decisions, resulting in greater uncertainty in the efficiency and effect of the transfer, which may affect the treatment effect. Summary of the invention

[0003] The present disclosure provides a method, device, equipment, medium and program product for transferring wounded persons. The technical solution of the present disclosure is as follows:

[0004] In a first aspect, the present disclosure provides a method for transporting a wounded person, comprising:

[0005] Get the current disaster information corresponding to the current disaster;

[0006] Determine the simulated casualty information and the rescue capacity of the rescue site corresponding to the current disaster information;

[0007] Generate a casualty transfer strategy based on the simulated casualty information and the treatment capacity of the treatment location; wherein the casualty transfer strategy is at least one;

[0008] Simulate each of the wounded transport strategies to obtain a transport index for each of the wounded transport strategies;

[0009] According to the transfer index of each of the wounded transfer strategies, a target wounded transfer strategy is determined, so as to perform wounded transfer based on the target wounded transfer strategy.

[0010] In a possible implementation manner, determining the simulated injured person information and the rescue capability of the rescue site corresponding to the current disaster information includes:

[0011] Obtain disaster data corresponding to each of the multiple disaster types; wherein the disaster data includes historical disaster types, historical disaster levels, and historical casualty information; wherein the casualty information includes the number of casualties and the distribution of injuries;

[0012] Determine simulated casualty information corresponding to each of the disaster types based on the disaster data corresponding to each of the multiple disaster types by using a Monte Carlo simulation method;

[0013] Obtaining treatment data corresponding to multiple treatment locations;

[0014] The Markov chain is used to evaluate the rescue capability of each of the rescue locations based on the rescue data corresponding to each of the multiple rescue locations.

[0015] In a possible implementation, generating a casualty transfer strategy based on the simulated casualty information and the treatment capability of the treatment location includes:

[0016] Get real-time traffic information;

[0017] Based on the real-time road condition information, the simulated wounded person information and the treatment capacity of the treatment location, a transfer path that meets preset conditions is formulated for each wounded person through a path planning algorithm; wherein the preset conditions are at least one and the transfer path for each wounded person is at least one;

[0018] A wounded person transfer strategy is generated based on the transfer path of each wounded person.

[0019] In a possible implementation, the simulating each of the wounded transport strategies to obtain a transport index for each of the wounded transport strategies includes:

[0020] Each of the wounded transfer strategies is simulated by the Monte Carlo simulation method to obtain a transfer index for each of the wounded transfer strategies; wherein the transfer index includes at least one of a transfer time, a rescue rate, and a utilization rate of rescue resources.

[0021] In a possible implementation, determining a target wounded transport strategy according to the transport index of each wounded transport strategy includes:

[0022] Evaluating the transport capacity of each transport strategy based on the transport index by a decision analysis method; wherein the decision analysis method includes at least one of an index comparison method and a statistical analysis method;

[0023] Based on the transport capacity of each of the transport strategies, a target casualty transport strategy is determined.

[0024] In a possible implementation, it further includes:

[0025] The transfer data is displayed according to a preset display method; wherein the transfer data includes each of the wounded transfer strategies, the transfer indicators of each of the wounded transfer strategies, the wounded transfer situation, and the treatment capacity of each of the treatment locations.

[0026] In a second aspect, the present disclosure provides a wounded transport device, comprising:

[0027] A data acquisition module is used to obtain current disaster information corresponding to the current disaster;

[0028] An evaluation module, used to determine the simulated casualty information corresponding to the current disaster information and the rescue capacity of the rescue site;

[0029] A strategy generation module, used to generate a wounded transfer strategy based on the simulated wounded information and the treatment capacity of the treatment location; wherein the wounded transfer strategy is at least one;

[0030] A simulation module, used to simulate each of the wounded transport strategies and obtain a transport index for each of the wounded transport strategies;

[0031] The transfer module is used to determine a target wounded transfer strategy according to the transfer indicators of each of the wounded transfer strategies, so as to transfer the wounded based on the target wounded transfer strategy.

[0032] In a third aspect, the present disclosure provides an electronic device, including:

[0033] processor;

[0034] a memory for storing instructions executable by the processor;

[0035] The processor is configured to execute the instructions to implement the method described in the first aspect.

[0036] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0037] In a fifth aspect, the present disclosure provides a computer program product, comprising a computer program / instructions, characterized in that when the computer program / instructions are executed by a processor, the method described in the first aspect is implemented.

[0038] The technical solution disclosed in this disclosure brings at least the following beneficial effects:

[0039] In the embodiment of the present disclosure, by obtaining the current disaster information corresponding to the current disaster; determining the simulated wounded information and the treatment capacity of the treatment site corresponding to the current disaster information; generating a wounded transfer strategy based on the simulated wounded information and the treatment capacity of the treatment site; wherein the wounded transfer strategy is at least one; simulating each of the wounded transfer strategies to obtain the transfer index of each of the wounded transfer strategies; determining the target wounded transfer strategy according to the transfer index of each of the wounded transfer strategies, so as to transfer the wounded based on the target wounded transfer strategy. In this way, the wounded transfer strategy can be dynamically and objectively planned by combining the wounded information of different disaster information and the treatment capacity of the treatment site, so that the determination of the wounded transfer strategy is more objective, and the subjective dependence is reduced, thereby improving the transfer efficiency and effect. Moreover, the wounded transfer strategy can also be simulated and selected based on the transfer index, so that the determined wounded transfer strategy is more in line with actual needs, further improving the transfer efficiency and effect, and further improving the treatment effect.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0042] Figure 1 A schematic diagram of a flow chart of a wounded transport method provided in an embodiment of the present disclosure;

[0043] Figure 2 It is a schematic diagram of a casualty transfer simulation process provided by an embodiment of the present disclosure;

[0044] Figure 3 is a schematic diagram of a transport deduction device provided by an embodiment of the present disclosure;

[0045] Figure 4 is a structural schematic diagram of a wounded transport device provided by an embodiment of the present disclosure;

[0046] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0049] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0050] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of national laws and regulations.

[0051] It should be noted that in the embodiments of the present disclosure, there may be existing solutions in the industry such as certain software, components, models, etc., which should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present disclosure, but it does not mean that the applicant has or will necessarily use the solution.

[0052] In the related technologies, natural disasters are of many types, widely distributed and frequent. Various accidents, disasters, public health emergencies and social security incidents also occur from time to time, such as earthquakes, floods, fires, explosions, etc. These disasters often cause a large number of casualties. After the disaster occurs, how to quickly and effectively transfer the wounded to a suitable treatment location for treatment is a major problem. In the face of mass casualties, emergency medical response is the core of many response strategies and means. When responding to mass casualties, the purpose of medical rescue response is to use limited medical resources to treat as many wounded and sick as possible. After the wounded and sick are determined to be diverted, they need to be transferred to a suitable medical institution for comprehensive medical treatment. This process involves the selection of transportation tools and the location of the medical institution for evacuation. At present, in the scene of medium and large-scale disasters, the decision-making of the transportation of batches of wounded often depends on the macro-evacuation strategy, which mainly depends on the personal experience and judgment of the on-site commander. Although some methods have studied the simulation of the transportation of wounded based on traditional dynamic models, they mainly consider time factors such as the minimum evacuation of the designated treatment location, and rarely consider factors such as the severity of the wounded's condition and the dynamic changes in the treatment capacity of the treatment location. Thus, the wounded transfer method in the related art has at least the following technical problems:

[0053] First, there is a lack of dynamic consideration of the treatment capacity of the treatment site. Traditional methods usually evaluate the treatment capacity of the treatment site based only on static indicators such as the number of beds and the number of medical staff at the treatment site, without considering the dynamic changes in the treatment capacity of the treatment site after the disaster. For example, with the continuous influx of the wounded, the consumption of materials at the treatment site, the fatigue of medical staff and other factors will cause the treatment capacity of the treatment site to gradually decline, but the traditional method cannot reflect this change in a timely manner.

[0054] Second, there is a lack of predictability of the treatment capacity of the treatment site. It is impossible to predict the treatment capacity of the treatment site in the future, which makes it lack of foresight when arranging the transfer of the wounded. The wounded may be transferred to a treatment site that originally had certain treatment capacity but was overwhelmed when the wounded arrived, thus delaying the treatment of the wounded.

[0055] Third, the transfer decision lacks scientificity. Traditional decisions on the transfer of the wounded often rely on the experience of the on-site commanders, lacking scientific analysis and quantitative basis. Different commanders may make different decisions, resulting in greater uncertainty in the efficiency and effect of the transfer. For example, usually only a single factor such as distance is considered to select the transfer treatment location, without comprehensively considering multiple factors such as the treatment capacity of the treatment location, the severity of the wounded's injuries, and traffic conditions. This may cause the wounded to be transferred to a treatment location that is closer but not suitable for their injuries, affecting the treatment effect.

[0056] Fourth, simulation is insufficient. Traditional methods may perform some simple simulations, but they often lack systematicity and comprehensiveness. For example, they may only simulate traffic conditions, but not conduct comprehensive simulations of the conditions of the injured, the results of triage, and the treatment locations. Traditional simulations may rely mainly on manual calculations or simple mathematical models, which cannot accurately reflect complex disaster scenarios and dynamic changes.

[0057] Moreover, in medium- and large-scale disaster scenarios, different types of disasters will produce different distributions of casualties. For example, earthquakes may cause a large number of casualties with fractures, and fires may cause casualties with burns and respiratory injuries. Moreover, the injuries and number of casualties are constantly changing, and the treatment capacity of the treatment site will change over time. For example, the treatment site may experience a shortage of medical resources and a decrease in treatment capacity due to the increase in the number of casualties. At the same time, the traffic conditions at the disaster site will also affect the efficiency of the transfer of the casualties. If the time-varying treatment capacity and traffic conditions of the treatment site cannot be accurately grasped, it will be difficult to make reasonable decisions on the transfer of the casualties. Therefore, there is an urgent need for a new method for the transfer of casualties that can simulate the distribution and triage results of casualties of different types of disasters in medium- and large-scale disaster scenarios, and combine the time-varying treatment capacity and traffic conditions of the treatment site to provide scientific and reasonable decision support for the transfer of casualties for rescue personnel, thereby improving the efficiency and success rate of the treatment of the casualties.

[0058] Based on this, the embodiment of the present disclosure provides a method for transferring the wounded, which can be applied to the emergency treatment and transfer decision support of the wounded after the occurrence of medium and large-scale disasters such as natural disasters and accident disasters, and improves the efficiency and success rate of the treatment of the wounded by integrating medical resources and optimizing the transfer path. The method for transferring the wounded provided by the embodiment of the present disclosure can simulate and deduce the wounded conditions of different disaster types, formulate targeted transfer strategies according to the specific disaster types, and improve the rescue effect. Combined with the attributes of the treatment site, historical patients, changes in the number of beds and other data, a time-varying treatment capacity model of the treatment site is constructed, and then Markov chain and Monte Carlo are used to simulate the treatment site receiving the wounded in a non-empty state, and the wounded transfer plan is dynamically planned to realize the transfer deduction and plan optimization of the wounded in the disaster scene. Moreover, the Markov chain can accurately describe the dynamic changes of the treatment capacity of the treatment site. By dividing the treatment capacity state of the treatment site into different levels and defining the state transition probability matrix, the process of the treatment capacity of the treatment site from one state to another can be tracked in real time, providing a dynamic basis for the decision of the transfer of the wounded. Based on historical data and current status, the future treatment capacity of the treatment site can be predicted, which is very important for planning the transfer route of the wounded and selecting the appropriate treatment site in advance, and can avoid transferring the wounded to the treatment site that is about to be saturated or cannot provide effective treatment. At the same time, Monte Carlo simulation can effectively deal with the randomness and uncertainty factors in disaster scenarios. By randomly generating a large number of simulation scenarios, various possible disaster situations and distribution of the wounded can be covered, so as to more comprehensively evaluate the effects of different transfer strategies. At the same time, the uncertainty of factors such as the number of wounded, the severity of injuries, and traffic conditions is considered, making the simulation results more realistic and reliable. At the same time, Monte Carlo simulation can perform statistical analysis on a large number of simulation results to obtain the probability distribution and statistical characteristics of various indicators. For example, the expected value and variance of indicators such as the average treatment time and mortality rate can be calculated to evaluate the risks and benefits of different transfer strategies.

[0059] The technical solutions provided by various embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0060] Figure 1 The flowchart of a method for transferring a wounded person provided by an embodiment of the present disclosure is shown in FIG. 1 , and the method can be applied to a server, such as a single server or a server cluster. Figure 1 As shown, the casualty transfer method may include the following steps:

[0061] S101, obtaining current disaster information corresponding to the current disaster.

[0062] In an embodiment of the present disclosure, when a disaster occurs, disaster information corresponding to the current disaster, that is, current disaster information, can be determined. Exemplarily, the disaster information may include information such as the type of disaster, the location of the incident, the scope of impact, the duration, and the time of the incident. The type of disaster may include, for example, earthquakes, mudslides, floods, fires, and the like. It is understandable that in some implementation scenarios, the disaster information may also include a disaster level. For example, when the disaster type is an earthquake, the disaster level may be an earthquake level.

[0063] S102, determining the simulated casualty information corresponding to the current disaster information and the rescue capacity of the rescue site.

[0064] In an embodiment of the present disclosure, after the current disaster information is acquired, the simulated casualty information corresponding to the current disaster information can be determined. Considering that for the same or similar disaster information, the generated casualty information is usually not much different, it is possible to perform statistical analysis based on the data about the casualty information in the historical disaster data to obtain the simulated casualty information corresponding to the current disaster information. The historical disaster data may include historical casualty information data corresponding to a plurality of historical disaster information. Based on the historical casualty information data corresponding to a plurality of historical disaster information, the casualty information corresponding to each disaster information can be obtained. The casualty information may include, for example, the number of casualties, the injury condition of each casualty, the injured part of each casualty, etc.

[0065] After obtaining the current disaster information, the treatment capacity of the treatment location corresponding to the current disaster information can also be determined. Exemplarily, the treatment information of the treatment location can be obtained. The treatment information can include, for example, the location of the treatment location, the level of the treatment location, the number of treatment units, the number of beds, the number of operating rooms, the number of treatment personnel, the status of treatment equipment, and other information. The treatment location can include, for example, medical institutions such as hospitals. It can be understood that the treatment information can also include historical treatment data, including information such as treatment time and success rate of different injuries. Then, the treatment capacity of each treatment location can be obtained based on the treatment information of the treatment location. As an example, the treatment capacity can be indicated in the form of an indicator value. For example, the indicator system method can be used to construct indicators such as bed utilization rate, workload of medical staff, and medical supplies reserves to comprehensively evaluate the treatment capacity status of the treatment location. The time series analysis method can also be used to predict the changing trend of the treatment capacity of the treatment location. At the same time, considering factors such as the inflow of wounded and the consumption of medical resources, dynamic treatment capacity information is provided for transfer decisions.

[0066] S103, generating a casualty transfer strategy based on simulated casualty information and the treatment capabilities of the treatment locations.

[0067] In an embodiment of the present disclosure, after obtaining the simulated wounded information and the treatment capacity of the treatment site, a wounded transfer strategy can be generated. Exemplarily, a wounded transfer strategy can be generated based on the simulated wounded information and the treatment capacity information of the treatment site. As an example, the wounded transfer strategy can be one, two or more. For example, the wounded transfer strategy can be n1 wounded transferred to treatment site A, n2 wounded transferred to treatment site B, and n3 wounded transferred to treatment site C. It can be understood that the wounded transfer strategy can also include wounded information, such as injury status, injured part, etc. Considering that the current transfer method is usually ambulance transfer, if there are other transfer methods, other transfer methods can also be considered.

[0068] S104, simulating each wounded patient transfer strategy to obtain a transfer index for each wounded patient transfer strategy.

[0069] In the embodiments of the present disclosure, after the wounded transfer strategy is generated, the wounded transfer strategy can be simulated to obtain simulation results. Exemplarily, different wounded transfer strategies can be simulated by building a simulation engine, for example, the transfer process of each wounded transfer strategy can be simulated, or the generation of wounded information and treatment at the treatment location can be simulated to obtain the transfer index of each wounded transfer strategy, for example, the transfer time spent by each wounded transfer strategy.

[0070] S105, determining a target wounded transfer strategy according to the transfer index of each wounded transfer strategy, and performing wounded transfer based on the target wounded transfer strategy.

[0071] In an embodiment of the present disclosure, after obtaining the transfer index of each wounded transfer strategy, the recommended wounded transfer strategy, i.e., the target wounded transfer strategy, can be determined based on the transfer index of each wounded strategy. After determining the target wounded transfer strategy, the target wounded transfer strategy can be output so that relevant personnel can view the target wounded transfer strategy to transfer the wounded based on the target wounded transfer strategy. It is understandable that when outputting, other wounded transfer strategies, as well as simulation data and transfer indexes of each wounded transfer strategy can also be output simultaneously on the basis of outputting the target wounded transfer strategy. The target wounded transfer strategy can also be determined by relevant personnel based on the simulation data and transfer indexes of each wounded transfer strategy.

[0072] In the embodiment of the present disclosure, by obtaining the current disaster information corresponding to the current disaster; determining the simulated wounded information and the treatment capacity of the treatment site corresponding to the current disaster information; generating a wounded transfer strategy based on the simulated wounded information and the treatment capacity of the treatment site; wherein the wounded transfer strategy is at least one; simulating each of the wounded transfer strategies to obtain the transfer index of each of the wounded transfer strategies; determining the target wounded transfer strategy according to the transfer index of each of the wounded transfer strategies, so as to transfer the wounded based on the target wounded transfer strategy. In this way, the wounded transfer strategy can be dynamically and objectively planned by combining the wounded information of different disaster information and the treatment capacity of the treatment site, so that the determination of the wounded transfer strategy is more objective, and the subjective dependence is reduced, so as to improve the transfer efficiency and effect. Moreover, the wounded transfer strategy can also be simulated and selected based on the transfer index, so that the determined wounded transfer strategy is more in line with the actual needs, further improving the transfer efficiency and effect, and further improving the treatment effect.

[0073] In some possible implementations, determining simulated casualty information corresponding to current disaster information and the rescue capability of a rescue location includes:

[0074] Obtain disaster data corresponding to various disaster types; the disaster data includes historical disaster types, historical disaster levels, and historical casualty information; the casualty information includes the number of casualties and the distribution of injuries;

[0075] Through the Monte Carlo simulation method, based on the disaster data corresponding to various disaster types, the simulated casualty information corresponding to each disaster type is determined;

[0076] Obtaining treatment data corresponding to multiple treatment locations;

[0077] Through the Markov chain, the treatment capacity of each treatment location is evaluated based on the treatment data corresponding to multiple treatment locations.

[0078] In the embodiments of the present disclosure, a Monte Carlo simulation method may be used to determine simulated casualty information corresponding to the current disaster information. Exemplarily, disaster data corresponding to each of the multiple disaster types may be obtained, such as historical disaster types, historical disaster levels, and historical casualty information, and the casualty information may include the number of casualties and the distribution of injuries. Then, the Monte Carlo simulation method is used to determine simulated casualty information corresponding to each disaster type based on the disaster data corresponding to each of the multiple disaster types. As an example, the simulation process includes the following process:

[0079] (1) Number of simulated casualties:

[0080] Based on historical data and probability distribution, the statistical information on the number of casualties in historical disaster type data (such as earthquakes, mudslides, floods, fires, etc.) is used to determine the probability distribution of the number of casualties under different types of disaster information. Then, the number of casualties under different disaster information can be generated from these probability distributions by random sampling, and a probability distribution model that conforms to the actual situation can be determined. For example, Poisson distribution can be used to generate the number of casualties under different disaster information from these probability distributions by random sampling. At the same time, the intensity of the disaster, the population density of the affected area, the type of building, and the impact of different time periods on the information of the casualties can also be considered.

[0081] (2) Simulated distribution of casualties:

[0082] This step can classify the injuries of the wounded in multiple dimensions according to the actual situation. For example, it can be classified according to the site of injury: craniocerebral injury, maxillofacial injury, neck injury, chest (back) injury, abdominal (waist) injury, pelvic injury, spinal cord injury, limb injury and multiple injuries, etc.; classified according to skin integrity: closed injury, open injury; classified according to injury information description: trauma, burns, fractures, poisoning and other related injury information descriptions. Then, the corresponding probability distribution can be determined for each injury category, and a specific injury distribution can be generated through random sampling. As follows:

[0083] Related to the type of disaster: Different types of disasters often lead to different injury distributions. For example, earthquake disasters may cause a large number of fractures and crush injuries, while fire disasters may cause burns and respiratory injuries. Monte Carlo simulation can be used to adjust the probability model of injury distribution according to the characteristics of different disaster types to make it more in line with the actual situation. Moreover, historical data can be combined to determine the probability and severity of various injuries under different disaster types, thereby improving the pertinence and practicality of the simulation results.

[0084] Dynamic change simulation: Considering that the injuries of the wounded may change over time, a mathematical model can be established to simulate the evolution of the injuries of the wounded. The probability of change of the injuries of the wounded can be adjusted according to different treatment measures and time factors, providing a reference for the formulation of dynamic treatment and transfer plans.

[0085] (3) Monte Carlo simulation of the number of casualties and their distribution, including:

[0086] 1) Simulate the number of casualties. Assume that the average number of casualties λ is related to factors such as disaster type D, disaster level I, population density ρ, and occurrence time t, as follows:

[0087] λ=aD+bI+cρ+dt+e (1)

[0088] Among them, a, b, c, d, and e are coefficients to be determined, which can be estimated through historical data or experience. These coefficients can be estimated from historical disaster data through statistical analysis methods, such as the least squares method.

[0089] The number of casualties follows a Poisson probability distribution with parameter λ, as follows:

[0090]

[0091] Among them, X represents the number of casualties and k is a specific value.

[0092] Assuming the number of simulations is T, the Poisson distribution parameter λ can be determined based on the disaster type, disaster level, population density, occurrence time, etc. i , randomly generate the number of casualties N from a Poisson distribution i ,but Use a random number generator to implement this formula. Let the random number r start from k=0 and calculate When P>r, then k=N i .

[0093] 2) Injury distribution simulation. The severity of injuries, such as severe injuries, moderate injuries, and minor injuries, can be represented by S1, S2, and S3 respectively; the types of injuries, such as external injuries, internal injuries, and special injuries, can be represented by T1, T2, and T3 respectively; the parts of injuries, such as the head, chest, abdomen, and limbs, can be represented by P1, P2, P3, and P4 respectively.

[0094] The initial probability distribution of various injury combinations can be determined based on historical data, which is affected by factors such as disaster type and disaster level. For example, the probability of a serious injury with external injuries and the head as the injured part in an earthquake disaster is as follows:

[0095]

[0096] Among them, eq∈D represents the earthquake disaster type.

[0097] For each simulated casualty (N casualties), four random numbers r1, r2, and r3 in the interval [0,1] are generated, and the injury status is determined according to the following method:

[0098] Determine the severity of injury: Calculate the cumulative probability of various injury severity levels based on the disaster type and disaster intensity:

[0099]

[0100] in, is the probability of injury severity j under earthquake disaster intensity I.

[0101] if The severity of the injury is determined as Sk .

[0102] Determine the type of injury: Similar to the severity of the injury, the injury type is T m The probability of Calculate the cumulative probability and determine the type of injury based on r2.

[0103] Determine the injured part: The injured part is P m The probability of Calculate the cumulative probability and determine the injury site based on r3.

[0104] It is also possible to obtain the corresponding treatment data of multiple treatment locations, and evaluate the treatment capacity of each treatment location based on the corresponding treatment data of multiple treatment locations through a Markov chain. As an example, the treatment capacity evaluation process of a treatment location can be as follows:

[0105] (1) Data collection. Basic information about the incident can be entered into the system; information about the injured can be simulated and generated based on the incident information; information about the treatment resources at the treatment location can be imported, and basic information about the treatment location can be obtained based on the incident location. Ambulance integrated information is generated in batches, and sufficient transport resources can be assumed by default.

[0106] Among them, the information of the injured includes basic information and injury information. Injury severity: mild, moderate, severe, based on rapid triage assessment; classification by injury site: craniocerebral injury, maxillofacial injury, neck injury, chest (back) injury, abdominal (waist) injury, pelvic injury, spinal cord injury, limb injury and multiple injuries, etc.; classification by skin integrity: closed injury, open injury; injury information description: trauma, burns, fractures, poisoning and other related injury information description. The data of the injured is obtained by simulating triage based on different disaster types (determining the basic demographic characteristics of the injured, setting the injury spectrum of different events, generating the injured according to the generation probability and time series, and quickly classifying through the triage algorithm start or jumpstart algorithm).

[0107] (2) Variable definition and state space determination.

[0108] Consider factors such as the number of injured people, the number of emergency units, the number of operating rooms, the number of ICU beds, the number of ordinary beds, the number of medical staff, and the availability of key medical equipment. Variables related to the injured: the number of injured people, triage information; variables related to the emergency unit: the number of emergency units, the status of each emergency unit (busy / idle), etc. Variables related to operating rooms: the number of operating rooms, the number of operating rooms in use, etc. ICU-related variables: the number of ICU beds, the number of occupied ICU beds, etc. Ordinary bed-related variables: the number of ordinary beds, the number of ordinary beds in use, etc. Variables related to medical staff: the total number of medical staff, the number of medical staff distributed in each department, etc. Variables related to key medical equipment: the number of key medical equipment, the number of available key medical equipment, etc. Time variables: used to represent different time points.

[0109] According to the value range of the variables, the state space of the hospital's treatment capacity is determined. For the number of emergency units, it can be divided into several intervals, each corresponding to a state; similar divisions can be applied to the number of operating rooms, ICU beds, and ordinary beds. For the number of medical staff and the availability of key medical equipment, the state can be determined according to different levels. The different states of these variables are combined to form the overall state space of the hospital's treatment capacity. Let the state vector be , where represents the number of patients received at time , represents the number of emergency units at time , is the number of operating rooms, is the number of ICU beds, is the number of ordinary beds, is the number of medical staff, and is the number of key medical equipment.

[0110] (3) Probability transfer calculation.

[0111] New patient inflow rate: Consider the speed and probability distribution of patients from different sources flowing into the hospital, such as emergency patients, referred patients, etc.

[0112] Treatment progress and discharge speed of existing patients: Analyze the treatment progress and discharge probability of patients in different departments (emergency, operating room, ICU, general ward), which is usually related to the severity of the patient's condition, treatment method, recovery speed, etc. Number of medical staff: including the number of medical staff required in the emergency room, operating room, and ICU; key medical equipment: consider whether the number of key medical equipment meets the monitoring needs of the operating room and ICU; hospital resource allocation strategy: the hospital management's decision on resource allocation, such as adding emergency units, adjusting operating room arrangements, transferring patients, etc.

[0113] Based on the operation of the hospital in the past in a similar time period when the injured were treated at the treatment location (such as the hospital), the probability of transitioning from a specific combination of emergency units, operating rooms, ICUs, general beds, medical staff, and key medical equipment states to another state is estimated. Assume that the state space of the hospital's treatment capacity consists of multiple dimensions such as the number of injured states, the number of ICU beds states, the number of medical staff states, and the availability of medical equipment states. Each state variable can take different discrete values. The transition probabilities between all states are organized into a high-dimensional matrix, and the dimension of the matrix is ​​determined by the number of states of each variable. The elements in the matrix represent the probability of transitioning from a specific hospital treatment capacity state to another state.

[0114] The state is transferred from S(t) = {N(t), E(t), I(t), B(t), H(t), D(t)} to

[0115] The probability of S(t+1)={N(t+1),E(t+1),I(t+1),B(t+1),H(t+1),D(t+1)} is: P(S(t+1)S(t).

[0116] For each state variable, its transfer probability can be estimated by distribution. P(E(t+1)E(t) represents the probability that the number of emergency units E(t) transfers to E(t+1). For the change in the current number of wounded, it can be estimated based on the inflow and outflow of wounded N(t+1)=N(t)+λ in (t)-λ out (t).

[0117] Assume S i = {N i ,E i ,I i ,B i ,H i ,D i}Transfer to The number of times is n(S i ,S j ), the total number of transitions to any state is N(S i ), then the transition probability is estimated as: P(S j , S i )=n(S i , S j ) / N(S i ).

[0118] (4) Monte Carlo simulation.

[0119] In each iteration of the Monte Carlo simulation, the next state can be selected based on the transition probability. Assuming the current state is S(t), a state S(t+1) is randomly generated based on the transition probability distribution P(S(t+1)S(t)). All factors are synchronously updated to the time state of t+1. Define the hospital's treatment capacity index as C(t), then the treatment capacity index is the weighted sum of various resource factors:

[0120] C(t)=α N ×N(t)+α E ×E(t)+α O ×O(t)+α I ×I(t)+α H ×H(t)+α D ×D(t) (5)

[0121] Among them, the α series is the weight system of each factor, which can be determined according to the actual situation. The treatment capacity indicators of specific departments can be defined according to different needs. Taking ICU as an example, Among them, the β series is the corresponding weight coefficient, H ICU (t) represents the number of medical staff assigned to the ICU, D ICU (t) represents the number of key medical equipment assigned to the ICU. Similarly, the emergency department and operating room are similar.

[0122] After multiple Monte Carlo simulation iterations, the hospital treatment capacity index value in each iteration is recorded. The expected value, variance and other statistics of the index can be calculated to evaluate the uncertainty and change trend of the hospital treatment capacity. The expected value of the hospital treatment capacity index is: Where N is the number of simulations, C (t) (i) is the hospital treatment capacity index value at time t in the i-th simulation.

[0123] In some possible implementations, based on simulated casualty information and the rescue capabilities of the rescue site, a casualty transfer strategy is generated, including:

[0124] Get real-time traffic information;

[0125] Based on real-time traffic information, simulated casualty information, and the treatment capacity of the treatment location, a transfer route that meets preset conditions is formulated for each casualty through a path planning algorithm; wherein the preset condition is at least one, and each casualty has at least one transfer route;

[0126] Generate a casualty transfer strategy based on the transfer path of each casualty.

[0127] In the embodiments of the present disclosure, when generating the wounded transfer strategy, real-time road condition information can also be obtained, such as current traffic information, such as road traffic conditions, including whether the road is unobstructed, whether there is congestion, the specific location and degree of congestion; traffic accident information, such as traffic accidents occurring on the road, including the type, location, impact range and whether the road is closed or slowed down; construction and maintenance information, such as information on road construction or maintenance, including the specific section of the construction, construction time, impact on traffic and expected completion time. Traffic control measures information, such as traffic control measures such as road closure, traffic restriction, speed limit, and the reasons and time for implementing these measures. Then, on the basis of simulating the wounded information and the treatment capacity of the treatment site, the real-time road condition information can be further combined to use the path planning algorithm to formulate a transfer route for each wounded, and the transfer route can meet the preset conditions, such as the preset conditions can be one or more of the factors such as the shortest path, the fastest arrival time, and the lowest traffic risk. After that, the wounded transfer strategy can be generated based on the transfer path of each wounded. For example, we can comprehensively consider multiple goals such as the effect of treating the wounded, the time of transportation, and the traffic risk, and formulate a strategy for the transportation of the wounded. We can integrate optimization algorithms such as genetic algorithms and simulated annealing algorithms to solve multi-objective optimization problems. We can consider different disaster scenarios and needs, and adjust the weights of the optimization goals to meet the actual transportation decision-making needs. Understandably, we can also update the traffic conditions and hospital treatment capacity information in real time, dynamically adjust the transportation route, and ensure that the wounded can reach the appropriate treatment location as soon as possible for treatment. In this way, the accuracy, transportation efficiency, and effect of the wounded transportation strategy can be further improved.

[0128] In a possible implementation, each wounded transport strategy is simulated to obtain a transport index for each wounded transport strategy, including:

[0129] Each wounded transfer strategy is simulated by the Monte Carlo simulation method to obtain the transfer index of each wounded transfer strategy; wherein the transfer index includes at least one of the transfer time, the rescue rate, and the utilization rate of rescue resources.

[0130] Accordingly, according to the transfer indicators of each casualty transfer strategy, the target casualty transfer strategy is determined, including:

[0131] The transport capacity of each transport strategy is evaluated based on the transport index by using a decision analysis method; wherein the decision analysis method includes at least one of an index comparison method and a statistical analysis method;

[0132] Determine the target casualty transfer strategy based on the transfer capacity of each transfer strategy.

[0133] In the embodiments of the present disclosure, Monte Carlo simulation and other methods can be used to construct a simulation engine to simulate the process of wounded transfer, simulate multiple links such as wounded generation and hospital treatment, consider various uncertain factors, and evaluate the effects of different transfer strategies. For example, Monte Carlo simulation and other methods can be used to construct a simulation engine to simulate each wounded transfer strategy. Through multiple simulation runs, the statistical results of each wounded transfer strategy can be obtained to provide a reliable reference for transfer decisions. The statistical results of each wounded transfer strategy can include transfer indicators, such as transfer time, treatment rate, and utilization rate of treatment resources. Based on this, methods such as indicator comparison method and statistical analysis method can be used to evaluate the advantages and disadvantages of different wounded transfer strategies based on the transfer indicators of each wounded transfer strategy. According to the evaluation results, the target wounded transfer strategy can be determined, and feedback and suggestions can be provided for transfer decisions to continuously optimize the transfer strategy.

[0134] In a possible implementation, the method for transporting a wounded person may further include:

[0135] The transfer data is displayed according to a preset display method; wherein the transfer data includes each wounded transfer strategy, the transfer index of each wounded transfer strategy, the wounded transfer situation, and the treatment capacity of each treatment location.

[0136] In the disclosed embodiments, data visualization can also be performed. Exemplarily, the transfer data can be displayed according to a preset display method. For example, the transfer data can be visualized in the form of intuitive charts, maps, etc. For example, a map can be used to display the location of the wounded, the distribution of hospitals, traffic conditions, and transfer routes; a bar chart can be used to represent the distribution of the number of wounded with different injuries; and a line chart can be used to show the changing trend of the hospital's treatment capacity. It is understandable that data filtering and interactive functions can also be provided, and users can select specific disaster scenarios, types of wounded, or hospitals to view according to their needs, so as to better understand the data and analysis results. In this way, the results of the simulation can be displayed in the form of animations or dynamic charts, so that users can intuitively understand the effects of different transfer strategies, including showing the changes in the position of the wounded during the transfer process, the dynamic changes in the hospital treatment situation, and the traffic conditions.

[0137] In order to make the wounded transfer method provided by the embodiment of the present disclosure clearer, the following is a description with reference to specific examples. The details are as follows:

[0138] The wounded transfer method provided by the disclosed embodiment can simulate and deduce the wounded conditions of different disaster types, formulate targeted transfer strategies according to specific disaster types, and improve the rescue effect. Combined with data such as hospital attributes, historical patients, and changes in the number of beds, a hospital time-varying treatment capacity model is constructed, and then Markov chain and Monte Carlo are used to simulate the hospital receiving wounded in a non-empty state, and the wounded transfer plan is dynamically planned to achieve the transfer deduction and plan optimization of the wounded in the disaster scenario. The Markov chain can accurately describe the dynamic changes of the hospital's treatment capacity. By dividing the hospital's treatment capacity state into different levels and defining the state transition probability matrix, the process of the hospital's treatment capacity transferring from one state to another can be tracked in real time, providing a dynamic basis for the decision-making of the wounded transfer. Based on historical data and current status, the future treatment capacity state of the hospital can be predicted, which is very important for planning the wounded transfer route in advance and selecting a suitable hospital, and can avoid transferring the wounded to a hospital that is about to be saturated or unable to provide effective treatment. Moreover, Monte Carlo simulation can effectively deal with randomness and uncertainty factors in disaster scenarios; by randomly generating a large number of simulation scenarios, various possible disaster situations and distribution of casualties can be covered, so as to more comprehensively evaluate the effectiveness of different strategies for the transfer of casualties; at the same time, the uncertainty of factors such as the number of casualties, the severity of injuries, and traffic conditions is taken into account, making the simulation results more realistic and reliable. At the same time, Monte Carlo simulation can perform statistical analysis on a large number of simulation results to obtain the probability distribution and statistical characteristics of various indicators. For example, the expected value and variance of indicators such as the average treatment time and mortality rate can be calculated to evaluate the risks and benefits of different transfer strategies. As an example, the method for transferring casualties provided in the embodiments of the present disclosure includes the following processing procedures:

[0139] 1. Transshipment deduction method

[0140] Data collection and analysis include: disaster scenario information, including disaster type, location, impact range, duration, etc.; hospital information, including hospital location, hospital grade, number of emergency units, number of ICU beds, number of operating rooms, medical staff, medical equipment status, etc.; casualty triage information, including the number of casualties, severity of injuries, injured parts, etc.; transportation tools, including license plate number, ambulance type, transportation capacity, etc.

[0141] The hospital's time-varying treatment capacity assessment can be based on the collected hospital information, combined with the impact of disasters on the hospital and the allocation of resources within the hospital, to evaluate the hospital's treatment capacity at different time points in real time. The Markov Monte Carlo simulation method is used to construct a hospital's time-varying treatment capacity model that takes into account the emergency unit, operating room, ICU, and medical staff resource elements.

[0142] The planning of the transfer route for the wounded includes planning the best transfer route for the wounded based on the hospital's time-varying treatment capacity and the distribution of the wounded, using the path planning algorithm. Factors considered include traffic conditions, road capacity, hospital reception capacity, transfer time, etc. For seriously injured patients, priority is given to planning to hospitals that are closer and have stronger treatment capabilities; for lightly injured patients, they can be reasonably allocated according to the hospital's load to ensure that the wounded can reach the appropriate hospital for treatment as soon as possible.

[0143] The transfer simulation includes the use of computer simulation technology to simulate the transfer process of the wounded. During the simulation, the hospital's treatment capacity and the status of the wounded are continuously updated according to the set time step, simulating the transfer process of the wounded, the hospital's reception and the treatment process. Through multiple simulations, the effects of different transfer strategies are analyzed, such as transfer time, treatment success rate, medical resource utilization rate, etc., to provide reference for decision-making.

[0144] Decision optimization and implementation, including optimizing the transfer strategy and selecting the best transfer plan based on the results of the transfer simulation. In the decision-making process, the weight of various factors is comprehensively considered, such as changes in the wounded's condition, the rational use of medical resources, and transfer efficiency. The optimized transfer plan is implemented in the actual disaster relief work, and the transfer strategy is continuously adjusted and improved through real-time monitoring and feedback to ensure that the wounded can receive timely and effective treatment.

[0145] like Figure 2 As shown, Figure 2 This is a flow chart of casualty transfer simulation provided by the embodiment of the present disclosure. Figure 2 , the casualty transfer simulation method includes the following processing:

[0146] 1. Simulate the sorting and distribution of casualties based on disaster information, including:

[0147] (1) Simulated number of casualties;

[0148] (2) simulated injury distribution;

[0149] (3) Monte Carlo simulation of the number of casualties and the distribution of their injuries;

[0150] 2. Construction of hospital time-varying treatment capacity model, including:

[0151] (1) Data collection;

[0152] (2) variable definition and state space determination;

[0153] (3) Probabilistic transfer calculation;

[0154] (4) Monte Carlo simulation.

[0155] (II) Transport simulation device

[0156] The transfer simulation device is designed to provide efficient and accurate decision support for the transfer of casualties in medium- and large-scale disaster scenarios. It integrates advanced sensor technology, data analysis algorithms and visualization tools, and can monitor hospital treatment capabilities, the condition of the casualties and traffic conditions in real time, and perform transfer simulation to optimize the transfer strategy of the casualties. Figure 3 The transport simulation device includes a data acquisition module, a data processing and analysis module, a transport decision module, a simulation simulation module, and a visualization display module. Exemplary:

[0157] (1) Data acquisition module, used for:

[0158] Simulation generation of casualties: Based on different disaster types and historical data, Monte Carlo simulation and other methods are used to generate information such as the number of casualties, injury distribution, and injured parts. Factors such as disaster intensity and population density in the affected area are considered to ensure that the simulated casualties are authentic and representative. Each simulated casualty is assigned a unique identifier (injury ticket) for subsequent tracking and analysis.

[0159] Hospital treatment data collection: Use sensors and data interfaces or information reporting methods to collect real-time data related to hospital treatment capabilities, such as the number of hospital beds, the number of medical staff, and the status of medical equipment. Collect historical treatment data of the hospital, including treatment time and success rate for different injuries.

[0160] (2) Data processing and analysis module, used for:

[0161] Hospital treatment capacity assessment: Based on the collected hospital treatment data, the data analysis algorithm is used to evaluate the hospital's current treatment capacity. The indicator system method is used to construct indicators such as bed utilization rate, medical staff workload, and medical supplies reserves to comprehensively evaluate the hospital's treatment capacity status. The time series analysis method is used to predict the changing trend of the hospital's treatment capacity. At the same time, factors such as the influx of wounded and the consumption of medical resources are considered to provide dynamic hospital treatment capacity information for transfer decisions.

[0162] Assessment of the injury status of the injured: Analyze the collected historical data of the injured to assess the severity of the injured's injuries. For example, a machine learning algorithm can be used to classify the injured according to their vital signs, injury site and type, and determine the level of injury, such as severe, moderate and minor. Combined with the results of the hospital's treatment capacity assessment, determine the best hospital and priority for each injured person.

[0163] (3) Transshipment decision module, used to:

[0164] Path planning: Based on the patient's condition and the hospital's treatment capacity, a path planning algorithm is used to develop the best transfer route for each patient. The shortest path, fastest arrival time, and lowest traffic risk can be considered to select the optimal transfer route.

[0165] Traffic conditions and hospital treatment capacity information are updated in real time, and transfer routes are adjusted dynamically to ensure that the wounded can reach the appropriate hospital for treatment as quickly as possible.

[0166] Multi-objective optimization: Comprehensively consider multiple objectives such as the treatment effect of the wounded, the transfer time, and the traffic risk, and formulate the optimal transfer strategy for the wounded. Integrate optimization algorithms such as genetic algorithms and simulated annealing algorithms to solve multi-objective optimization problems. Consider different disaster scenarios and needs, and adjust the weights of the optimization objectives to meet the actual transfer decision needs.

[0167] (4) Simulation module, used for:

[0168] Simulation engine: Using Monte Carlo simulation and other methods, we built a simulation engine to simulate the casualty transfer process in different disaster scenarios. We simulated multiple links such as casualty generation and hospital treatment, considered various uncertain factors, and evaluated the effects of different transfer strategies.

[0169] It supports large-scale simulation and obtains statistically significant results through multiple simulation runs, providing a reliable reference for transshipment decisions.

[0170] Effect evaluation: Evaluate the results of the simulation and analyze indicators such as the treatment effect and transfer time of the wounded under different transfer strategies. Use methods such as indicator comparison and statistical analysis to evaluate the advantages and disadvantages of different strategies. Based on the effect evaluation results, provide feedback and suggestions for transfer decisions and continuously optimize the transfer strategy.

[0171] (5) Visualization display module

[0172] Data visualization: Display the collected data and analysis results in intuitive charts, maps, etc. Use maps to show the location of the injured, hospital distribution, traffic conditions and transfer routes; use bar charts to show the number of injured people with different injuries; use line charts to show the changing trend of hospital treatment capabilities. Provide data filtering and interactive functions, users can select specific disaster scenes, types of injured people or hospitals to view according to their needs, so as to better understand the data and analysis results.

[0173] Simulation result display: The simulation results are displayed in the form of animations or dynamic charts to intuitively understand the effects of different transfer strategies, including the changes in the position of the wounded during the transfer process, the dynamic changes in hospital treatment and traffic conditions.

[0174] Multi-angle display: observe the simulation results from different perspectives, including global perspective, local perspective and single casualty perspective, so as to deeply analyze the problems and optimization directions in the transfer process.

[0175] Decision support interface: Design a user-friendly decision support interface to provide the information and tools required for transshipment decisions. The interface includes a data display area, a simulation result area, a decision parameter setting area, and a decision suggestion area. You can adjust the decision parameters on the interface, including target weight, number of simulations, etc., view the decision results and suggestions in real time, and make accurate transshipment decisions quickly.

[0176] In summary, the wounded transport method provided by the embodiment of the present disclosure constructs a hospital time-varying treatment capacity model based on the Markov chain, which can fully consider the dynamic changes in the hospital's reception and treatment response speed, and consider the hospital's time-varying treatment capacity as an important factor, considering the current hospital resource status such as the number of emergency unit beds, the number of ICU beds, the number of ordinary beds, the number of medical staff, the availability of medical equipment, etc. The hospital's treatment capacity state is divided into different levels, the state transition probability matrix is ​​defined, and the change of the hospital's treatment capacity is predicted by the iterative calculation of the Markov chain. This model construction method can more realistically reflect the dynamic changes of the hospital's treatment capacity. Monte Carlo simulation can also be used to randomly generate a large number of different disaster scenarios and wounded conditions, simulate and deduce each scenario, and obtain more reliable results through statistical analysis. This method increases the diversity and accuracy of the simulation, can adapt to various medium and large-scale disaster scenarios, such as earthquakes, floods, mudslides, fires, epidemics, etc., and automatically adjusts the transportation strategy and plan according to the characteristics and impact of the disaster scene. It has strong adaptability and flexibility, and provides a more comprehensive reference for formulating the optimal transportation strategy. At the same time, a visual simulation device was built to display the results of Markov chain and Monte Carlo simulation in the form of intuitive charts, maps, etc. Users can use the platform to view the transfer of the wounded, the status of the hospital's treatment capacity, and various statistical analysis results in real time, making it easier to make decisions. The device has a built-in automatic optimization and scheduling system, which automatically generates the optimal scheduling plan for the transfer of the wounded based on the simulation results and real-time data. This can quickly respond to changes in disaster conditions, adjust the transfer strategy in a timely manner, and improve the transfer efficiency.

[0177] The specific implementation and technical effects of each step of this embodiment are similar to those of the above method embodiments, and will not be repeated here.

[0178] Based on the same inventive concept, the embodiment of the present disclosure also provides a wounded transport device. Figure 4 As shown, the wounded transport device 400 includes:

[0179] The data acquisition module 410 is used to acquire current disaster information corresponding to the current disaster;

[0180] An evaluation module 420 is used to determine simulated casualty information corresponding to the current disaster information and the rescue capacity of the rescue site;

[0181] A strategy generation module 430 is used to generate a wounded transport strategy based on the simulated wounded information and the treatment capacity of the treatment location; wherein the wounded transport strategy is at least one;

[0182] A simulation module 440 is used to simulate each of the wounded transport strategies to obtain a transport index for each of the wounded transport strategies;

[0183] The transfer module 450 is used to determine a target wounded transfer strategy according to the transfer indicators of each of the wounded transfer strategies, so as to perform wounded transfer based on the target wounded transfer strategy.

[0184] In a possible implementation, the evaluation module 420 is used to:

[0185] Obtain disaster data corresponding to each of the multiple disaster types; wherein the disaster data includes historical disaster types, historical disaster levels, and historical casualty information; wherein the casualty information includes the number of casualties and the distribution of injuries;

[0186] Determine simulated casualty information corresponding to each of the disaster types based on the disaster data corresponding to each of the multiple disaster types by using a Monte Carlo simulation method;

[0187] Obtaining treatment data corresponding to multiple treatment locations;

[0188] The Markov chain is used to evaluate the rescue capability of each of the rescue locations based on the rescue data corresponding to each of the multiple rescue locations.

[0189] In a possible implementation, the strategy generation module 430 is used to:

[0190] Get real-time traffic information;

[0191] Based on the real-time road condition information, the simulated wounded person information and the treatment capacity of the treatment location, a transfer path that meets preset conditions is formulated for each wounded person through a path planning algorithm; wherein the preset conditions are at least one and the transfer path for each wounded person is at least one;

[0192] A wounded person transfer strategy is generated based on the transfer path of each wounded person.

[0193] In a possible implementation, the simulation module 440 is used to:

[0194] Each of the wounded transfer strategies is simulated by the Monte Carlo simulation method to obtain a transfer index for each of the wounded transfer strategies; wherein the transfer index includes at least one of a transfer time, a rescue rate, and a utilization rate of rescue resources.

[0195] In a possible implementation, the transfer module 450 is used to:

[0196] Evaluating the transport capacity of each transport strategy based on the transport index by a decision analysis method; wherein the decision analysis method includes at least one of an index comparison method and a statistical analysis method;

[0197] Based on the transport capacity of each of the transport strategies, a target casualty transport strategy is determined.

[0198] In a possible implementation, it further includes:

[0199] The display module is used to display the transfer data according to a preset display method; wherein the transfer data includes each of the wounded transfer strategies, the transfer indicators of each of the wounded transfer strategies, the wounded transfer situation, and the treatment capacity of each of the treatment locations.

[0200] The specific implementation method and technical effect of the device provided in the embodiment of the present disclosure are similar to those of the above-mentioned method embodiment, and will not be repeated here.

[0201] According to an embodiment of the present disclosure, the present disclosure also discloses an electronic device, a computer-readable storage medium, and a computer program product.

[0202] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device 500 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0203] like Figure 5As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0204] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0205] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the wounded transport method. For example, in some embodiments, the wounded transport method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the wounded transport method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the wounded transport method in any other appropriate manner (e.g., by means of firmware).

[0206] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0207] The program code of the computer program product for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0208] In the context of the present disclosure, a computer-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0209] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0210] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0211] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0212] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0213] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for transporting a wounded person, characterized in that: include: Get the current disaster information corresponding to the current disaster; Determine the simulated casualty information and the rescue capacity of the rescue site corresponding to the current disaster information; Generate a casualty transfer strategy based on the simulated casualty information and the treatment capacity of the treatment location; wherein the casualty transfer strategy is at least one; Simulate each of the wounded transport strategies to obtain a transport index for each of the wounded transport strategies; According to the transfer index of each of the wounded transfer strategies, a target wounded transfer strategy is determined, so as to perform wounded transfer based on the target wounded transfer strategy.

2. The method for transporting the wounded according to claim 1, characterized in that: The determining of the simulated injured person information and the rescue capability of the rescue location corresponding to the current disaster information includes: Obtain disaster data corresponding to each of the multiple disaster types; wherein the disaster data includes historical disaster types, historical disaster levels, and historical casualty information; wherein the casualty information includes the number of casualties and the distribution of injuries; Determine simulated casualty information corresponding to each of the disaster types based on the disaster data corresponding to each of the multiple disaster types by using a Monte Carlo simulation method; Obtaining treatment data corresponding to multiple treatment locations; The Markov chain is used to evaluate the rescue capability of each of the rescue locations based on the rescue data corresponding to each of the multiple rescue locations.

3. The method for transporting the wounded according to claim 1, characterized in that: The generating of a wounded transfer strategy based on the simulated wounded information and the treatment capability of the treatment location includes: Get real-time traffic information; Based on the real-time road condition information, the simulated wounded person information and the treatment capacity of the treatment location, a transfer path that meets preset conditions is formulated for each wounded person through a path planning algorithm; wherein the preset conditions are at least one and the transfer path for each wounded person is at least one; A wounded person transfer strategy is generated based on the transfer path of each wounded person.

4. The method for transporting the wounded according to claim 1, characterized in that: The simulating of each of the wounded transport strategies to obtain the transport index of each of the wounded transport strategies includes: Each of the wounded transfer strategies is simulated by the Monte Carlo simulation method to obtain a transfer index for each of the wounded transfer strategies; wherein the transfer index includes at least one of a transfer time, a rescue rate, and a utilization rate of rescue resources.

5. The method for transporting the wounded according to claim 4, characterized in that: Determining a target wounded transport strategy according to the transport index of each wounded transport strategy includes: Evaluating the transport capacity of each transport strategy based on the transport index by a decision analysis method; wherein the decision analysis method includes at least one of an index comparison method and a statistical analysis method; Based on the transport capacity of each of the transport strategies, a target casualty transport strategy is determined.

6. The method for transporting the wounded according to claim 4, characterized in that: Also includes: The transfer data is displayed according to a preset display method; wherein the transfer data includes each of the wounded transfer strategies, the transfer indicators of each of the wounded transfer strategies, the wounded transfer situation, and the treatment capacity of each of the treatment locations.

7. A wounded transport device, characterized in that: include: A data acquisition module is used to obtain current disaster information corresponding to the current disaster; An evaluation module, used to determine the simulated casualty information corresponding to the current disaster information and the rescue capacity of the rescue site; A strategy generation module, used to generate a wounded transfer strategy based on the simulated wounded information and the treatment capacity of the treatment location; wherein the wounded transfer strategy is at least one; A simulation module, used to simulate each of the wounded transport strategies and obtain a transport index for each of the wounded transport strategies; The transfer module is used to determine a target wounded transfer strategy according to the transfer indicators of each of the wounded transfer strategies, so as to transfer the wounded based on the target wounded transfer strategy.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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