Artificial intelligence-based medical space adaptability flat-to-acute conversion method

Abstract

The present application belongs to the field of medical conversion, and particularly relates to a medical space adaptive conversion method based on artificial intelligence. The method first acquires medical demand severity, medical conversion space and medical strategy library. Secondly, according to the medical demand severity, the medical conversion space configuration is triggered to determine the conversion key, and the corresponding medical conversion strategy scheme is obtained from the medical conversion strategy library. The present application realizes efficient and intelligent conversion between the normal state and the emergency state through the conversion key and the medical conversion space, meets the medical service demand and the rapid acquisition of the medical emergency scheme in different scenarios, and improves the utilization efficiency of medical resources and the emergency response capability.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of medical space conversion, and particularly relates to a medical space adaptive conversion method based on artificial intelligence. BACKGROUND

[0002] Emergencies such as large-scale infectious disease epidemics and major disasters will cause a sharp increase in the number of wounded and sick, which poses a severe challenge to the carrying capacity, layout and resource allocation of medical spaces. In an emergency, conventional medical spaces need to be quickly converted into emergency medical spaces to meet the treatment of a large number of wounded and sick, and in normal times, they need to return to conventional services and reasonably allocate resources. However, the traditional medical space design and management mode is relatively static and cannot quickly complete the conversion between normal and emergency states. In particular, in terms of medical equipment allocation, current methods rely on manual experience and manual records, lack intelligent and accurate allocation systems, and cannot adaptively switch between normal and emergency states and adjust emergency classification strategies according to demand conditions. Therefore, the present application proposes a medical space adaptive conversion method based on artificial intelligence. SUMMARY

[0003] To overcome the deficiencies of the prior art, the present application provides a medical space adaptive conversion method based on artificial intelligence, which first acquires the medical demand severity, the medical normal-emergency conversion space and the medical normal-emergency strategy library, and then triggers the normal-emergency conversion discrimination key of the medical normal-emergency conversion space configuration according to the medical demand severity, and acquires the medical normal-emergency strategy scheme corresponding to the demand from the configured medical normal-emergency strategy library. The present application realizes efficient and intelligent conversion between normal and emergency states through the normal-emergency conversion discrimination key and the medical normal-emergency conversion space, meets the medical service demand and rapid acquisition of medical emergency schemes in different scenarios, and improves the utilization efficiency of medical resources and emergency response capability.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] The medical space adaptive conversion method based on artificial intelligence comprises:

[0006] acquiring the medical demand severity, the medical normal-emergency conversion space and the medical normal-emergency strategy library;

[0007] triggering the normal-emergency conversion discrimination key of the medical normal-emergency conversion space configuration according to the medical demand severity, and acquiring the medical normal-emergency strategy scheme corresponding to the demand from the configured medical normal-emergency strategy library;

[0008] The medical normal-emergency conversion space comprises an emergency degree configuration subspace, an emergency dispatch simulation subspace and a normal state simulation subspace.

[0009] The emergency degree configuration subspace, the emergency dispatch simulation subspace and the normalization simulation subspace are connected with each other through a flat emergency conversion judgment key.

[0010] Specifically, the emergency degree configuration subspace is configured with a comprehensive fuzzy evaluation model for evaluating the input explicit state set to obtain the corresponding medical demand severity;

[0011] The emergency degree configuration subspace comprises a first flat emergency judgment key for judging whether to enter an emergency state according to the medical demand severity at the current time;

[0012] The first flat emergency judgment key is connected with a corresponding hierarchical emergency trigger sub-key of each type of health emergency response module and disaster emergency response module in the emergency dispatch simulation subspace through a configured hierarchical emergency trigger chain;

[0013] The first flat emergency judgment key is connected with the normalization simulation subspace through a configured normal chain;

[0014] The hierarchical emergency trigger sub-key of each type of health emergency response module and disaster emergency response module is connected with the normalization simulation subspace through a configured set of flat emergency conversion sub-chains, and a flat emergency judgment sub-key is configured in each flat emergency conversion sub-chain;

[0015] The hierarchical emergency trigger sub-key of each type of health emergency response module or disaster emergency response module is one-to-one mapped with the medical demand severity of the corresponding type of public health event or disaster event through a configured hierarchical trigger mapping relationship.

[0016] Specifically, a degradation trigger point is configured between the hierarchical emergency trigger sub-key of each type of health emergency response module or disaster emergency response module;

[0017] The degradation trigger point is used for real-time evaluation of the medical demand severity of the public health event or disaster event corresponding to the current type of health emergency response module or disaster emergency response module, and performs degradation trigger strategy mapping according to the real-time evaluation result and the configured hierarchical trigger mapping relationship;

[0018] Each type of health emergency response module or disaster emergency response module comprises a same number of health emergency response sub-modules or disaster emergency response sub-modules as the corresponding hierarchical emergency trigger sub-key;

[0019] Each hierarchical emergency trigger sub-key is one-to-one corresponding to each health emergency response sub-module or disaster emergency response sub-module;

[0020] The health emergency response sub-modules or disaster emergency response sub-modules corresponding to each type of health emergency response module or disaster emergency response module are connected through the degradation trigger point;

[0021] The medical emergency strategy library is connected with the medical emergency conversion space through a configured dual hierarchical path model;

[0022] The dual hierarchical path model includes a first hierarchical emergency strategy search sub-model and a second normal strategy search sub-model;

[0023] Each health emergency response sub-module and disaster emergency response sub-module is connected with a front storage node in the medical emergency strategy library through the first hierarchical emergency strategy search sub-model;

[0024] The normal simulation sub-space is connected with the medical emergency strategy library through the second normal strategy search sub-model.

[0025] Specifically, the obtaining step of the hierarchical trigger mapping relationship includes:

[0026] Obtaining a set of explicit states corresponding to different types of public health events or disaster events and a set of medical resource configuration distribution states corresponding thereto;

[0027] According to the set of explicit states corresponding to the corresponding type of public health event or disaster event, the medical demand severity score of the corresponding type of public health event or disaster event is obtained through a comprehensive fuzzy evaluation algorithm;

[0028] The medical severity level of the corresponding type of public health event or disaster event is obtained by combining the medical demand severity score of the corresponding type of public health event or disaster event with a preset public health event or disaster event evaluation level interval;

[0029] According to the set of medical resource configuration distribution states under different medical severity levels, the medical resource emergency degree score is obtained, and according to the configured medical resource demand level interval, the medical demand emergency level is obtained.

[0030] Specifically, the obtaining step of the hierarchical trigger mapping relationship further includes:

[0031] Setting a set of medical severity level discrimination thresholds for each type of public health event or disaster event and embedding them into the first emergency discrimination key;

[0032] Embedding the medical resource demand level interval corresponding to the medical demand emergency level into the hierarchical emergency trigger sub-key constructed by the corresponding type of health emergency response sub-module or disaster emergency response sub-module;

[0033] Based on the medical severity level discrimination threshold set, the medical severity level of the corresponding type of public health event or disaster event is initially classified, and the emergency medical severity level and the normal severity level corresponding to each type of public health event or disaster event are obtained.

[0034] Based on the emergency medical severity level and the corresponding medical demand emergency level corresponding to each type of public health event or disaster event, a one-to-one mapping function between the medical severity level and the medical demand emergency level is established by a support vector machine kernel function, and a hierarchical trigger mapping relationship is obtained.

[0035] Similarly, based on the normal severity level and the corresponding medical demand emergency level, a normal level mapping relationship is obtained.

[0036] The hierarchical trigger mapping relationship is mapped to the hierarchical emergency trigger chain, and the normal level mapping relationship is mapped to the ordinary chain, and the medical configuration simulation trigger of the corresponding type of public health event or disaster event is performed, and the medical emergency strategy scheme corresponding to the corresponding type of public health event or disaster event is obtained.

[0037] Specifically, each health emergency response sub-module, disaster emergency response sub-module and normal simulation sub-space under the emergency dispatch simulation sub-space are configured with a medical emergency strategy simulation evolution model, which is used to perform medical emergency strategy simulation evolution evaluation and update according to the medical severity level, medical demand emergency level and medical resource configuration distribution state of the corresponding region corresponding to different types of public health events or disaster events.

[0038] The step of obtaining the medical emergency strategy scheme corresponding to the corresponding type of public health event or disaster event includes:

[0039] Real-time acquisition of the explicit state set corresponding to the corresponding type of public health event or disaster event in the current region and the medical resource configuration distribution state corresponding to the current region;

[0040] Based on the real-time acquired explicit state set, the medical demand severity and medical severity level corresponding to the current type of public health event or disaster event are obtained through the emergency degree configuration sub-space.

[0041] The medical severity level corresponding to the current type of public health event or disaster event is input into the first emergency judgment key.

[0042] When the medical severity level corresponding to the current type of public health event or disaster event is less than the corresponding medical severity level discrimination threshold, it is determined that the medical severity level corresponding to the current type of public health event or disaster event is a normal severity level, and the normal level mapping relationship in the ordinary chain is triggered.

[0043] Specifically, the step of obtaining the medical emergency strategy scheme corresponding to the type of public health event or disaster event further comprises:

[0044] According to the second normal strategy search sub-model between the normalization simulation subspace and the medical emergency strategy library, the medical emergency strategy scheme corresponding to the current normal severity level is searched;

[0045] The step of searching the medical emergency strategy scheme corresponding to the current normal severity level comprises:

[0046] According to the current normal severity level, the corresponding medical demand emergency level is obtained through the normal level mapping relationship;

[0047] Based on the medical demand emergency level, the second normal strategy search sub-model is used to match search the pre-stored node in the medical emergency strategy library, to determine whether there is a medical emergency strategy scheme corresponding to the same medical demand emergency level as the current type of public health event or disaster event;

[0048] If so, the corresponding medical emergency strategy scheme is fed back to the medical emergency strategy simulation evolution model configured in the normalization simulation subspace as the current first normal medical strategy scheme;

[0049] Meanwhile, combined with the explicit state set corresponding to the current type of public health event or disaster event and the medical resource configuration distribution state corresponding to the current region, the evolution simulation of the current medical emergency strategy scheme is performed to obtain the fit degree score of the first normal medical strategy scheme and the medical resource configuration distribution state corresponding to the current type of public health event or disaster event and the current region.

[0050] Specifically, the step of searching the medical emergency strategy scheme corresponding to the current normal severity level further comprises:

[0051] A fit degree threshold is set, and if the fit degree score corresponding to the first normal medical strategy scheme is greater than the fit degree threshold, the first normal medical strategy scheme is configured to the current region as the corresponding medical emergency strategy scheme;

[0052] If the fit degree score corresponding to the first normal medical strategy scheme is less than or equal to the fit degree threshold, the corresponding difference point information in the first normal medical strategy scheme is used to combine the explicit state set corresponding to the current type of public health event or disaster event and the medical resource configuration distribution state corresponding to the current region, and the second normal strategy search sub-model is used to conditionally map search the medical emergency strategy library to obtain the second normal medical strategy scheme;

[0053] feedback the second normal medical strategy scheme to the normalization simulation subspace, and repeat the evolutionary simulation and fitness degree discrimination process of the first normal medical strategy scheme to obtain a normal medical strategy scheme satisfying a fitness degree score greater than a fitness degree threshold value;

[0054] If not, directly use the explicit state set corresponding to the current type of public health event or disaster event, the medical resource allocation distribution state corresponding to the current region, and the corresponding medical demand emergency level to search from the medical emergency strategy library through the second normal strategy search submodel, and combine the fitness degree threshold value through the normalization simulation subspace to obtain a normal medical strategy scheme satisfying a fitness degree score greater than a fitness degree threshold value.

[0055] Specifically, the step of obtaining the medical emergency strategy scheme corresponding to the type of public health event or disaster event further comprises:

[0056] When the medical severity level corresponding to the current type of public health event or disaster event is greater than or equal to the corresponding medical severity level discrimination threshold value, it is determined that the medical severity level corresponding to the current type of public health event or disaster event is an emergency medical severity level;

[0057] Based on the emergency medical severity level, trigger the current type of public health event or disaster event trigger mapping relationship built in the corresponding hierarchical emergency trigger sub-key in the hierarchical emergency trigger chain;

[0058] According to the current type of public health event or disaster event trigger mapping relationship, trigger the hierarchical emergency trigger sub-key configured by the corresponding health emergency response submodule or disaster emergency response submodule;

[0059] Through the current triggered hierarchical emergency trigger sub-key, call the first hierarchical emergency strategy search submodel to combine the explicit state set corresponding to the current type of public health event or disaster event, the medical resource allocation distribution state corresponding to the current region, the corresponding medical demand emergency level, and the secondary medical demand emergency level corresponding to the current medical demand emergency level. Through the same process as obtaining the normal medical strategy scheme, a first emergency medical strategy scheme satisfying the medical demand emergency level and a fitness degree score greater than the fitness degree threshold value, N corresponding secondary emergency medical strategy schemes, and a normal medical strategy scheme are obtained.

[0060] Specifically, the step of obtaining the medical emergency strategy scheme corresponding to the type of public health event or disaster event further comprises:

[0061] The first emergency medical strategy scheme meeting the condition is fed back to the corresponding health emergency response submodule or disaster emergency response submodule, and N secondary emergency medical strategy schemes and a normal medical strategy scheme are saved to the front storage node;

[0062] The medical severity level and the medical demand emergency level corresponding to the health emergency response submodule or the disaster emergency response submodule configured with the first emergency medical strategy scheme are acquired in real time, and when the real-time medical demand emergency level of the current health emergency response submodule or the disaster emergency response submodule is less than the medical demand emergency level corresponding to the hierarchical emergency trigger sub-key built in the current health emergency response submodule or the disaster emergency response submodule,

[0063] The degradation trigger mapping is performed through the degradation trigger point configured between the health emergency response submodule or the disaster emergency response submodule corresponding to the current type of public health event or disaster event,

[0064] The corresponding secondary emergency medical strategy scheme stored in the front storage node is called according to the medical demand emergency level after degradation through the degradation trigger mapping and the first hierarchical emergency strategy search sub-model,

[0065] The emergency medical strategy scheme includes a medical resource distribution configuration sub-strategy of the current region and a missing medical resource rapid configuration and calling sub-strategy corresponding to the current region,

[0066] The degradation trigger mapping process is repeated, and the medical severity level corresponding to the health emergency response submodule or the disaster emergency response submodule is evaluated in real time,

[0067] When the medical severity level corresponding to the current type of public health event or disaster event is less than the medical severity level threshold of the corresponding type of public health event or disaster event built in the first emergency discrimination key, the normal strategy search sub-model is used to call the normal medical strategy scheme corresponding to the current type of public health event or disaster event stored in the front storage node through the emergency-normal conversion sub-chain between the current health emergency response submodule or the disaster emergency response submodule and the normal simulation subspace, by using the emergency-normal discrimination sub-key in the emergency-normal conversion sub-chain.

[0068] Compared with the prior art, the present application has the following advantages:

[0069] The present application is directed to the deficiencies of the prior art, by constructing a medical flat emergency conversion space, using flat emergency conversion discriminant keys and hierarchical emergency trigger chains, realizing efficient linkage between different subspaces, quickly completing flat emergency state switching, and the simulation evolution model built in different submodules realizes real-time adjustment of the obtained medical strategy scheme, improving the fit between the corresponding medical strategy scheme and the corresponding regional medical demand configuration. Secondly, according to the corresponding medical demand level conditions, intelligent precise medical strategy search configuration is carried out through the first hierarchical emergency strategy search submodel and the second normal strategy search submodel under the double hierarchical path model, realizing adaptive medical strategy configuration switching and rapid simulation evolution of fit. In addition, the setting of the degradation trigger point can adjust the hierarchical strategy in real time according to the severity of the medical demand, reduce unnecessary waste of medical resources, and comprehensively improve the intelligence and efficiency of medical space management. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 The present application is directed to the deficiencies of the prior art, by constructing a medical flat emergency conversion space, using flat emergency conversion discriminant keys and hierarchical emergency trigger chains, realizing efficient linkage between different subspaces, quickly completing flat emergency state switching, and the simulation evolution model built in different submodules realizes real-time adjustment of the obtained medical strategy scheme, improving the fit between the corresponding medical strategy scheme and the corresponding regional medical demand configuration. Secondly, according to the corresponding medical demand level conditions, intelligent precise medical strategy search configuration is carried out through the first hierarchical emergency strategy search submodel and the second normal strategy search submodel under the double hierarchical path model, realizing adaptive medical strategy configuration switching and rapid simulation evolution of fit. In addition, the setting of the degradation trigger point can adjust the hierarchical strategy in real time according to the severity of the medical demand, reduce unnecessary waste of medical resources, and comprehensively improve the intelligence and efficiency of medical space management.

[0071] Figure 2 The present application is directed to the deficiencies of the prior art, by constructing a medical flat emergency conversion space, using flat emergency conversion discriminant keys and hierarchical emergency trigger chains, realizing efficient linkage between different subspaces, quickly completing flat emergency state switching, and the simulation evolution model built in different submodules realizes real-time adjustment of the obtained medical strategy scheme, improving the fit between the corresponding medical strategy scheme and the corresponding regional medical demand configuration. Secondly, according to the corresponding medical demand level conditions, intelligent precise medical strategy search configuration is carried out through the first hierarchical emergency strategy search submodel and the second normal strategy search submodel under the double hierarchical path model, realizing adaptive medical strategy configuration switching and rapid simulation evolution of fit. In addition, the setting of the degradation trigger point can adjust the hierarchical strategy in real time according to the severity of the medical demand, reduce unnecessary waste of medical resources, and comprehensively improve the intelligence and efficiency of medical space management. DETAILED DESCRIPTION

[0072] The prior art in the aspect of medical resource allocation conversion currently relies on manual experience and manual recording, lacks intelligent precise allocation system, and cannot adaptively switch according to demand conditions and reduce unnecessary waste of medical resources. When facing emergency public health events or disaster events, it cannot realize rapid flat emergency conversion of medical resources, resulting in low efficiency of corresponding emergency strategies and inability to quickly convert and configure medical resources according to the severity of the corresponding event. For this purpose, please refer to Figure 1 and Figure 2 An embodiment provided by the present application: a medical space adaptive flat emergency conversion method based on artificial intelligence, comprising the following steps:

[0073] S1, obtaining medical demand severity, medical flat emergency conversion space and medical flat emergency strategy library;

[0074] S2, according to the medical demand severity, triggering the flat emergency conversion discriminant key configured by the medical flat emergency conversion space, and obtaining the medical flat emergency strategy scheme corresponding to the demand from the configured medical flat emergency strategy library.

[0075] Further, the medical flat emergency conversion space in the embodiment includes an emergency degree configuration subspace, an emergency dispatch simulation subspace and a normal simulation subspace;

[0076] Further, the emergency degree configuration subspace, the emergency dispatch simulation subspace and the normalization simulation subspace in the embodiment are connected with each other through the flat emergency conversion discrimination key.

[0077] Further, the emergency degree configuration subspace in the embodiment is configured with a comprehensive fuzzy evaluation model for evaluating the input explicit state set to obtain the corresponding medical demand severity;

[0078] Further, the explicit state set in the embodiment includes the type corresponding to the public health event or the disaster event, such as the public health event corresponding to a certain acute infectious disease or the disaster event type corresponding to a fire, a flood, an earthquake, a large-scale traffic accident, etc., the number of patients, the spread range, the disaster area, the number of casualties corresponding to the public health event or the disaster type, the number of deaths, the number of people with severe, moderate and mild illness or injury corresponding to the number of casualties, and the proportion of special groups such as the number and distribution of susceptible populations such as the elderly, children and pregnant women.

[0079] Further, the flat emergency conversion discrimination key in the embodiment includes a first flat emergency discrimination key, a hierarchical emergency trigger sub-key and a flat emergency discrimination sub-key.

[0080] The emergency degree configuration subspace includes the first flat emergency discrimination key for discriminating whether to enter an emergency state according to the medical demand severity at the current time, that is, whether to obtain the corresponding medical flat emergency strategy scheme through an ordinary chain or a hierarchical emergency trigger chain.

[0081] The first flat emergency discrimination key is connected with the hierarchical emergency trigger sub-key corresponding to each type of health emergency response module and disaster emergency response module in the emergency dispatch simulation subspace through the configured hierarchical emergency trigger chain;

[0082] The first flat emergency discrimination key is connected with the normalization simulation subspace through the configured ordinary chain.

[0083] The hierarchical emergency trigger sub-key corresponding to each type of health emergency response module and disaster emergency response module is connected with the normalization simulation subspace through the configured flat emergency conversion sub-chain set, and a flat emergency discrimination sub-key is configured in each flat emergency conversion sub-chain.

[0084] The hierarchical emergency trigger sub-key corresponding to each type of health emergency response module or disaster emergency response module and the medical demand severity of the corresponding type of public health event or disaster event are one-to-one mapped through the configured hierarchical trigger mapping relationship.

[0085] Further, the hierarchical emergency trigger sub-key corresponding to each type of health emergency response module or disaster emergency response module in the embodiment is configured with a degradation trigger point.

[0086] The degradation trigger point is configured to evaluate the medical demand severity of the public health event or disaster event corresponding to the current type of health emergency response module or disaster emergency response module in real time, and perform degradation trigger strategy mapping according to the real-time evaluation result and the configured hierarchical trigger mapping relationship.

[0087] Each type of health emergency response module or disaster emergency response module includes a same number of health emergency response sub-modules or disaster emergency response sub-modules as the corresponding hierarchical emergency trigger sub-key;

[0088] Each hierarchical emergency trigger sub-key corresponds to each health emergency response sub-module or disaster emergency response sub-module;

[0089] Further, the hierarchical emergency trigger chain, the emergency-ordinary conversion sub-chain set, and the ordinary chain in the embodiment are constructed by a graph algorithm, and the emergency degree configuration subspace, the emergency dispatch simulation subspace, and the normal simulation subspace are connected with each other; as shown in Figure 2 The dashed line connection between a3, b3, and m3 and the normal simulation subspace is the emergency-ordinary conversion sub-chain set.

[0090] Further, a1, a2, and a3 in the embodiment are health emergency response sub-modules or disaster emergency response sub-modules corresponding to different medical resource demand levels under the same type of health emergency response module or disaster emergency response module; for example, assuming that there is an acute infectious disease including 3 medical severity levels and corresponding 3 medical resource demand levels, each medical resource demand level corresponds to an acute infectious disease health emergency response sub-module.

[0091] Further, the grade size relationship of a1, a2, and a3 in the embodiment is a1≥a2≥a3; b1, b2, b3, m1, m2, and m3 have the same meaning as a1, a2, and a3, and the corresponding grade size relationship is also the same as a1, a2, and a3.

[0092] Each type of health emergency response module or disaster emergency response module is connected through the degradation trigger point between the corresponding health emergency response sub-module or disaster emergency response sub-module.

[0093] Furthermore, in this embodiment, the downgrade trigger point is implemented through a preset public health event or disaster event assessment level range. When the severity score of medical needs corresponding to the type of public health event or disaster event in the current submodule is less than the lower limit of the assessment level range to which the severity level of medical needs in the current submodule belongs, the current submodule is triggered to trigger the submodule corresponding to the next lower level of medical needs severity. For example, if the severity score of medical needs corresponding to the current type of public health event or disaster event is within the assessment level range corresponding to a1, but after processing by the configured medical routine / emergency strategy, the severity score of medical needs corresponding to the current public health event or disaster event is no longer within the assessment level range corresponding to a1, but is reduced to the assessment level range corresponding to a2, then the downgrade trigger point between a1 and a2 is triggered to adjust and modify the corresponding medical routine / emergency strategy, thereby reducing the waste of medical resources and personnel.

[0094] Furthermore, such as Figure 2 In the diagram, A, B, ..., M are labels corresponding to the types of public health events or disaster events.

[0095] Furthermore, in this embodiment, the medical routine / emergency strategy library is connected to the medical routine / emergency conversion space through a configured dual-level path model;

[0096] In this embodiment, the medical emergency strategy database is constructed using knowledge graph algorithms and graph databases. It stores detailed information about historical public health events or disasters, along with corresponding medical resources, building assessments and renovations, and specific implementation strategies. The database is equipped with an update algorithm to continuously update and store new medical emergency strategy plans generated by database searches and databases generated from external sources.

[0097] The dual-level path model includes a first-level emergency strategy search sub-model and a second-level normal strategy search sub-model.

[0098] Furthermore, in this embodiment, the first hierarchical emergency strategy search sub-model or the second normal strategy search sub-model is constructed using the pre-trained iFlytek Spark Deep Inference Model X1, DeepSeek-R1-Lite, DeepSeek-MoE, or AtomThink.

[0099] Each of the health emergency response submodules and disaster emergency response submodules is connected to the front-end storage node configured in the medical routine and emergency strategy library through the first hierarchical emergency strategy search sub-model.

[0100] The normalized simulation subspace is connected to the medical emergency strategy library through the second normalized strategy search sub-model.

[0101] Further, the obtaining step of the hierarchical trigger mapping relationship in the embodiment comprises:

[0102] obtaining a set of explicit states corresponding to different types of public health events or disaster events and a set of medical resource allocation distribution states corresponding thereto;

[0103] According to the set of explicit states corresponding to the corresponding type of public health event or disaster event, the medical demand severity score of the corresponding type of public health event or disaster event is obtained by a comprehensive fuzzy evaluation algorithm;

[0104] By combining the preset public health event or disaster event evaluation level interval with the medical demand severity score of the corresponding type of public health event or disaster event, the medical severity level of the corresponding type of public health event or disaster event is obtained.

[0105] According to the corresponding medical resource allocation distribution state set under different medical severity levels, the medical resource emergency degree score is obtained, and according to the configured medical resource demand level interval, the medical demand emergency level is obtained.

[0106] Further, the set of medical resource allocation distribution states under different medical severity levels in the embodiment is the medical resource information corresponding to the corresponding historical public health event or disaster event, such as the number of doctors, nurses and various medical drugs, instruments and total cost under the corresponding medical severity level.

[0107] Set the medical severity level discrimination threshold set of each type of public health event or disaster event and embed it into the first emergency discrimination key;

[0108] The medical resource demand level interval corresponding to the medical demand emergency level is embedded into the corresponding type of health emergency response submodule or disaster emergency response submodule to obtain a hierarchical emergency trigger submodule;

[0109] Based on the medical severity level discrimination threshold set, the medical severity level of the corresponding type of public health event or disaster event is initially divided to obtain the emergency medical severity level and the normal severity level corresponding to each type of public health event or disaster event;

[0110] Based on the emergency medical severity level and the corresponding medical demand emergency level of each type of public health event or disaster event, a one-to-one mapping function between the medical severity level and the medical demand emergency level is established by a support vector machine kernel function to obtain a hierarchical trigger mapping relationship;

[0111] Similarly, based on the normal severity level and the corresponding medical demand emergency level, a normal level mapping relationship is obtained.

[0112] The hierarchical trigger mapping relationship is mapped to the hierarchical emergency trigger chain, and the normal level mapping relationship is mapped to the ordinary chain, a corresponding type of public health event or disaster event medical configuration simulation trigger is performed, and a corresponding medical emergency strategy scheme for the corresponding type of public health event or disaster event is obtained.

[0113] Further, each health emergency response sub-module, disaster emergency response sub-module and normal simulation sub-space in the emergency dispatch simulation sub-space in the embodiment are configured with a medical emergency strategy simulation evolution model, which is used to perform medical emergency strategy simulation evolution evaluation and update according to the medical severity level, medical demand emergency level and medical resource configuration distribution state of the corresponding region corresponding to different types of public health events or disaster events.

[0114] Further, the medical emergency strategy simulation evolution model in the embodiment is constructed by a simulation simulation algorithm, and the medical resources corresponding to the corresponding region and the medical building layout data are input to perform three-dimensional simulation evolution simulation.

[0115] In terms of accurate decision-making, the explicit state set containing rich information such as event type, number of patients, and proportion of special groups is evaluated by the comprehensive fuzzy evaluation model to obtain the medical demand severity score, and the medical severity level of the event is determined in combination with the preset evaluation level interval. The medical demand emergency level is determined according to the medical resource configuration distribution state set under the level. Finally, the hierarchical trigger mapping relationship is established by means of support vector machine to provide a solid data foundation for subsequent accurate decision-making, so that the decision is no longer blind, but based on scientific analysis.

[0116] In terms of resource optimization allocation, the hierarchical trigger mapping relationship is used to determine the medical demand emergency level corresponding to the severity of different events, so as to match the appropriate medical resource allocation scheme from the medical emergency strategy library, realize accurate allocation of resources, avoid waste or shortage of resources, and when the medical demand severity decreases, the downgrade trigger point will trigger the lower level sub-module to adjust the strategy in time, further reducing unnecessary resource consumption.

[0117] Fast response and efficient switching depend on the first emergency trigger key, which can quickly determine whether to enter an emergency state according to the medical demand severity. Once it is determined to be in an emergency state, it is quickly connected to the corresponding hierarchical emergency trigger sub-key under the emergency dispatch simulation sub-space through the hierarchical emergency trigger chain, and then the corresponding sub-module is triggered to obtain and execute the emergency strategy from the medical emergency strategy library, greatly improving the emergency response speed and ensuring that a response can be made quickly in an emergency.

[0118] The dynamic strategy adjustment relies on the real-time evaluation of the medical demand severity by the degradation trigger point. Once the medical demand severity score is lower than the lower limit of the current sub-module evaluation level interval, the association between the current sub-module and the lower level sub-module will be triggered. At the same time, the medical emergency strategy simulation evolution model will evaluate and update the strategy according to the medical severity level, the medical demand emergency level, and the medical resource allocation distribution state of the corresponding area, to ensure that the strategy always conforms to the changes of the actual situation.

[0119] The knowledge reuse and experience accumulation in this embodiment benefit from the first hierarchical emergency strategy search sub-model and the second normal strategy search sub-model under the double hierarchical path model. They can search historical strategy schemes from the medical emergency strategy library to provide reference for current decision-making. In the practice process, new strategy schemes and experience are continuously stored in the strategy library, realizing continuous reuse of knowledge and continuous accumulation of experience, and continuously improving the ability to cope with various medical events.

[0120] In summary, the medical emergency conversion method improves the scientificity, efficiency and adaptability of medical emergency management through a series of interrelated mechanisms such as accurate decision derivation, resource optimization derivation, rapid response and efficient switching derivation, dynamic strategy adjustment derivation, and knowledge reuse and experience accumulation derivation, which can effectively protect the rational use of medical resources and cope with various medical events.

[0121] Further, the step of obtaining the medical emergency strategy scheme corresponding to the corresponding type of public health event or disaster event in the embodiment includes:

[0122] Real-time acquisition of the explicit state set corresponding to the corresponding type of public health event or disaster event in the current area and the medical resource allocation distribution state of the current area;

[0123] Further, the corresponding medical resource allocation distribution state in the embodiment mainly includes:

[0124] Medical personnel:

[0125] Number: The total number of various professional personnel such as doctors, nurses, pharmacists, and technicians, as well as the number of personnel in different departments (such as internal medicine, surgery, emergency department, obstetrics and gynecology, etc.).

[0126] Qualifications and titles: The proportion of chief physicians, deputy chief physicians, attending physicians, and resident physicians; the composition of senior nurses, supervising nurses, nurses, and nurses; and the number of personnel with various professional qualification certifications (such as first aid qualification certificate, specialist qualification certificate).

[0127] Workload: The average number of patients treated by each medical personnel per day, the number of beds nursed, and the continuous working time, overtime frequency, etc.

[0128] Medical devices:

[0129] Equipment list and quantity: such as the specific quantity of various equipment including CT, MRI, X-ray machine, B-ultrasound machine, ventilator, monitor, surgical instruments, etc.

[0130] Equipment status: The number and percentage of equipment in normal operation, under maintenance, and awaiting scrapping; the equipment's service life and estimated remaining service life.

[0131] Equipment distribution: The types and quantities of medical devices equipped in different departments, as well as the distribution of large equipment in different hospital areas and floors.

[0132] drug:

[0133] Drug inventory list: Names, dosage forms, and quantities of various common disease treatment drugs, emergency drugs, and special disease drugs (such as anticancer drugs and antipsychotic drugs).

[0134] Drug expiration dates: the quantity and proportion of drugs nearing their expiration date (expiring within 3-6 months), drugs with normal expiration dates, and the inventory warning quantity of key monitored drugs.

[0135] Drug procurement and supply: drug procurement cycle, supplier information, and recent stability and shortage of drug supply.

[0136] Regarding the conversion of building configurations within the medical area:

[0137] Ward configuration: number of general wards, intensive care unit (ICU) wards and isolation wards, and number of beds; ward area and layout (ratio of single, double and multi-person rooms); assessment of the amount of space that can be quickly converted into other functional wards (such as emergency infectious disease wards) and the difficulty of conversion.

[0138] Furthermore, this embodiment focuses on assessing the current medical building's ability to be converted into an emergency building, obtaining the corresponding conversion difficulty, the specific number of buildings that can be converted in the overall area, the number of buildings that need to be converted, the maximum emergency medical demand level corresponding to the converted emergency building, i.e., the maximum number of injured and sick people that can be accommodated, the maximum amount of resources that can be mobilized, etc.

[0139] Operating room configuration: number, area, and level of operating rooms (e.g., Class 100, Class 1000, Class 10000, Class 10000); equipment configuration of operating rooms; space and equipment resources that can be converted into emergency operating rooms in a short time, as well as the time and manpower costs required for conversion.

[0140] Outpatient area: The distribution and area of ​​outpatient departments, the size and capacity of waiting areas, registration and payment areas, and examination and testing areas; the space that can be flexibly adjusted into temporary emergency treatment areas, and the functional layout plan after the adjustment.

[0141] Passageway and logistics facilities: the width, length, and connectivity of passageways within the medical area, whether they meet the requirements for rapid evacuation and transportation of personnel and materials in emergency situations; the coverage, transportation capacity, and operating status of logistics transportation systems (such as pneumatic logistics and rail logistics), as well as the potential for modification to facilitate emergency material transportation.

[0142] Public facilities: the number, location, and usage of public facilities such as restrooms, hot water rooms, and rest areas; public space resources that can be temporarily modified to serve as auxiliary medical function areas (such as temporary material storage areas) in emergency situations.

[0143] Building structure and modification feasibility: the structural type of the medical building (such as frame structure, brick-concrete structure), load-bearing capacity, and seismic rating; the modifiability of building walls, floors, ceilings, and other structures, as well as the impact assessment on the safety of the building structure during the conversion between normal and emergency modes.

[0144] Further, in this embodiment, the building aspects in emergency conversion are exemplarily illustrated, including:

[0145] Ward configuration:

[0146] Basic data: detailed statistics on the number of ordinary wards, intensive care units (ICUs), and isolation wards, as well as the number of beds in each. For example, there are 50 ordinary wards with a total of 200 beds; there are 10 ICUs with 30 monitoring beds; there are 20 isolation wards with 60 beds. At the same time, the area of each ward is specified, such as 25 square meters per ordinary ward, 35 square meters per ICU, and 30 square meters per isolation ward. In terms of layout, single rooms account for 10% of ordinary wards, double rooms account for 60%, and multi-person rooms (4-6 people) account for 30%.

[0147] Emergency modification assessment: for spaces that can be quickly converted into emergency infectious disease wards, a comprehensive assessment of the modification difficulty is conducted. Factors to consider include the modification difficulty of the ventilation system of the original ward, the need for new installation of negative pressure ventilation equipment, and the difficulty coefficient is higher; the modification of water and electricity lines, and the judgment of whether large-scale re-laying is needed. The number of overall areas that can be modified is counted, assuming that there are 15 ordinary wards with modification potential. Determine the number of buildings that need to be modified, if 10 are planned to be modified to respond to a sudden outbreak. The maximum medical demand emergency level that the modified emergency building can accommodate is determined based on the facilities of the modified ward, and the maximum number of patients that can be accommodated is estimated, such as 80 infectious disease patients for the 10 modified wards. At the same time, the maximum mobilizable resource amount is evaluated, including the number of medical personnel, medical material reserves, etc., and it is expected that 20 professional medical personnel can be mobilized, and 30 days of use of protective materials and common medicines can be reserved.

[0148] Operating room configuration:

[0149] Basic Information: Accurately record the number of operating rooms, such as a total of 8 operating rooms. Mark the area of each operating room, 2 Class 100 operating rooms, each with an area of 50 square meters; 4 Class 1000 operating rooms, each with an area of 40 square meters; 2 Class 10000 operating rooms, each with an area of 35 square meters. List the equipment configuration of each operating room in detail, including the type and quantity of operating beds, shadowless lamps, anesthesia machines, surgical instruments, etc.

[0150] Emergency Conversion Assessment: Determine the space that can be converted into an emergency operating room in a short time, such as 2 Class 10000 operating rooms that can be quickly modified. Inventory the equipment resources needed for conversion, such as the need for additional allocation of mobile surgical equipment, temporary sterilization equipment, etc. Evaluate the time required for conversion, and estimate that through 24 hours of uninterrupted construction, one operating room can be converted; in terms of labor costs, 10 professional engineers and 5 medical staff need to be called upon to participate in equipment installation and debugging.

[0151] Outpatient Area:

[0152] Existing Layout: Clearly describe the distribution of outpatient departments, such as the specific floor and location of departments such as internal medicine, surgery, pediatrics, etc., as well as the area of each department. Calculate the size of the waiting area, registration and payment area, examination and testing area, such as the waiting area with an area of 500 square meters, which can accommodate 300 people at the same time; the registration and payment area has an area of 100 square meters and is equipped with 10 service windows; the examination and testing area has an area of 800 square meters. Clearly define the capacity of each area, such as the examination and testing area can receive 100 patients per hour.

[0153] Emergency Adjustment Plan: Plan the space that can be flexibly adjusted as a temporary emergency diagnosis and treatment area, such as modifying part of the idle administrative office area into a temporary diagnosis and treatment area. Develop the adjusted functional layout plan, set up triage area, initial diagnosis area, referral area, etc., to ensure smooth patient treatment process and avoid cross infection.

[0154] Passageway and Logistics Facilities:

[0155] Existing Condition: Accurately measure the width and length of the passageway in the medical area, such as the main passageway with a width of 3 meters and a length of 200 meters. Evaluate the connectivity of the passageway to ensure unobstructed passage between areas. Determine whether it meets the requirements of rapid evacuation and transportation of personnel and materials in emergency situations, such as whether the passageway has sufficient lighting and evacuation indicators. Clearly define the coverage range of the logistics transportation system (such as pneumatic logistics, track logistics), whether it covers all departments; calculate the transportation capacity, such as the pneumatic logistics can transport 50 times per hour; check the operation status and whether there are hidden dangers.

[0156] Emergency Modification Potential: Evaluate the modification potential for emergency material transportation, such as whether the transportation capacity can be improved by increasing the number of track logistics vehicles, and estimate the transportation capacity after modification.

[0157] Public Facilities:

[0158] Current statistics: Detailed statistics on the number and location of public facilities such as toilets, hot water rooms, rest areas, etc. For example, there are 2 male and female toilets on each floor, 1 hot water room, and 2 rest areas. Describe the usage status, such as the cleaning frequency of the toilets, the facility completion rate, and the daily usage rate of the rest areas.

[0159] Emergency reconstruction utilization: Determine the public space resources that can be temporarily reconstructed as medical auxiliary function areas (such as temporary material storage areas) in emergency situations, such as converting larger rest areas into material storage areas, which can store up to 100 cubic meters of medical supplies.

[0160] Building structure and reconstruction feasibility:

[0161] Structural information: Clearly state the structural type of the medical building, such as a frame structure. Explain the load-bearing capacity, such as 500 kg per square meter, and the seismic grade, which is 7 levels.

[0162] Reconstruction evaluation: Analyze the reconstructability of building walls, floors, ceilings, and other structures, such as the walls of frame structures that can be removed and reconstructed to facilitate the redivision of space. If the floor needs to be reinforced to support large medical equipment, assess the difficulty and cost of reinforcement. Assess the impact on the safety of the building structure during the conversion process, such as whether large-scale reconstruction requires additional support for the structure to ensure that the reconstructed building is safe and reliable in emergency use.

[0163] Based on the real-time acquisition of the explicit state set, the emergency degree configuration subspace is obtained, and the medical demand severity and medical severity level corresponding to the current type of public health event or disaster event are obtained;

[0164] The medical severity level corresponding to the current type of public health event or disaster event is input into the first flat emergency discrimination key;

[0165] When the medical severity level corresponding to the current type of public health event or disaster event is less than the corresponding medical severity level discrimination threshold, it is determined that the medical severity level corresponding to the current type of public health event or disaster event is the normal severity level, and the normal level mapping relationship in the ordinary chain is triggered.

[0166] According to the normal level mapping relationship, the second normal strategy search sub-model between the normal simulation subspace and the medical flat emergency strategy library is combined to search for the medical flat emergency strategy scheme corresponding to the current normal severity level.

[0167] The step of searching for the medical flat emergency strategy scheme corresponding to the current normal severity level includes:

[0168] According to the current normal severity level, the corresponding medical demand emergency level is obtained through the normal level mapping relationship.

[0169] Based on the medical demand emergency level, the second normal strategy search sub-model is used to search the pre-stored nodes in the medical emergency strategy library to determine whether there is a medical emergency strategy scheme with the same medical demand emergency level as the current type of public health event or disaster event;

[0170] If so, the corresponding medical emergency strategy scheme is fed back to the medical emergency strategy simulation evolution model configured in the normalization simulation subspace as the current first normal medical strategy scheme;

[0171] Meanwhile, the evolution simulation of the current medical emergency strategy scheme is performed in combination with the explicit state set corresponding to the current type of public health event or disaster event and the medical resource configuration distribution state corresponding to the current region, to obtain the fit degree score of the first normal medical strategy scheme and the medical resource configuration distribution state corresponding to the current type of public health event or disaster event and the current region;

[0172] A fit degree threshold is set, and if the fit degree score corresponding to the first normal medical strategy scheme is greater than the fit degree threshold, the first normal medical strategy scheme is configured to the current region as the corresponding medical emergency strategy scheme;

[0173] If the fit degree score corresponding to the first normal medical strategy scheme is less than or equal to the fit degree threshold, the corresponding difference point information in the first normal medical strategy scheme is used in combination with the explicit state set corresponding to the current type of public health event or disaster event and the medical resource configuration distribution state corresponding to the current region, to perform conditional mapping search on the medical emergency strategy library through the second normal strategy search sub-model, to obtain a second normal medical strategy scheme;

[0174] Further, the corresponding difference point information in the first normal medical strategy scheme in the embodiment is the resource configuration that does not match the demand medical resource configuration corresponding to the current normal severity level, such as personnel, medicine, equipment, and corresponding building space layout, etc.

[0175] The second normal medical strategy scheme is fed back to the normalization simulation subspace, and the evolution simulation and fit degree discrimination process of the first normal medical strategy scheme are repeated to obtain a normal medical strategy scheme that satisfies the fit degree score greater than the fit degree threshold;

[0176] If not, the explicit state set corresponding to the current type of public health event or disaster event and the medical resource configuration distribution state corresponding to the current region and the corresponding medical demand emergency level are directly used to search from the medical emergency strategy library through the second normal strategy search sub-model, and the normal medical strategy scheme that satisfies the fit degree score greater than the fit degree threshold is obtained through the cyclic evolution simulation and discrimination in the normalization simulation subspace in combination with the fit degree threshold.

[0177] Further, the specific steps of the cyclic evolution simulation discrimination and the evolution simulation discrimination process corresponding to the first normal medical strategy scheme and the second normal medical strategy scheme are the same, that is, the processes of fit degree evaluation discrimination and feedback updating.

[0178] This process ensures that the most appropriate medical emergency strategy scheme can be quickly and accurately formulated in the face of public health events or disasters through detailed medical resource allocation evaluation and dynamic adjustment mechanism. The core benefit is to achieve efficient use and flexible deployment of resources, which is specifically manifested as: after obtaining the explicit state set and the medical resource allocation distribution state of the corresponding area in real time, the system can quickly evaluate the medical demand severity of the current event and determine whether to enter the emergency state according to the preset threshold; for the normal state, the system matches similar cases in historical data to find the medical strategy scheme with the highest fit degree, and combines the actual situation to perform evolution simulation to ensure the actual feasibility of the scheme; when the existing scheme cannot meet the demand, the system will continuously optimize the search until the best solution is found. This closed-loop feedback mechanism based on data-driven not only improves the speed and accuracy of decision-making, but also effectively avoids resource waste, enhances the ability to respond to emergencies, and further improves the adaptability and flexibility of medical facilities through emergency conversion evaluation of building space layout, ensuring that high-quality medical services can still be provided in emergency situations, thereby protecting public health and social stability. Through the above method, the system can start an efficient evaluation process as soon as a public health event or disaster occurs, ensuring that all available resources are optimally allocated. In particular, detailed statistics and emergency reconstruction evaluation of medical personnel, medical equipment, drugs, and building space enable medical institutions to complete the transition from regular operation to emergency response in a short time, greatly improving the ability to respond to unexpected situations. In addition, through the cyclic iteration of evolution simulation and fit degree discrimination, the system can continuously learn and improve its strategy library, realizing self-evolution, which not only helps to improve future response efficiency, but also provides a scientific basis for long-term medical resource planning.

[0179] Further, the step of obtaining the medical emergency strategy scheme corresponding to the type of public health event or disaster event in the embodiment further comprises:

[0180] When the medical severity level corresponding to the current type of public health event or disaster event is greater than or equal to the corresponding medical severity level discrimination threshold, it is determined that the medical severity level corresponding to the current type of public health event or disaster event is an emergency medical severity level.

[0181] Trigger the current type public health event or disaster event trigger mapping relationship in the corresponding hierarchical emergency trigger sub-key built in the hierarchical emergency trigger chain triggered by the emergency medical severity level;

[0182] According to the current type public health event or disaster event trigger mapping relationship, trigger the hierarchical emergency trigger sub-key configured by the corresponding health emergency response sub-module or disaster emergency response sub-module;

[0183] Through the first hierarchical emergency strategy search sub-model triggered by the current hierarchical emergency trigger sub-key, combine the corresponding explicit state set of the current type public health event or disaster event, the corresponding medical resource configuration distribution state of the current area, the corresponding medical demand emergency level and the corresponding secondary medical demand emergency level of the current medical demand emergency level, through the same process as obtaining the normal medical strategy scheme, obtain the first emergency medical strategy scheme and the corresponding N secondary emergency medical strategy schemes and one normal medical strategy scheme that meet the medical demand emergency level and the compatibility score greater than the compatibility threshold;

[0184] Further, the embodiment is obtained through the same process as obtaining the normal medical strategy scheme, that is, first through the retrieval of the pre-stored node, if there is an emergency medical strategy scheme corresponding to the same type and same level public health event or disaster event, the corresponding medical strategy scheme is obtained, and through the same cyclic evolution simulation process as the first normal medical strategy scheme, the compatibility of the obtained emergency medical strategy scheme is discriminated and updated through the medical emergency strategy simulation evolution model built in the corresponding sub-module; At the same time, the N secondary emergency medical strategy schemes and one normal medical strategy scheme corresponding to the same type public health event or disaster event are synchronously retrieved and compatibility discriminated and updated, and the N secondary emergency medical strategy schemes are stored in the pre-stored node, and when the downgrade trigger point is triggered, the corresponding emergency medical strategy scheme of the corresponding health emergency response sub-module or disaster emergency response sub-module is quickly called;

[0185] The first emergency medical strategy scheme and the N secondary emergency medical strategy schemes correspond one-to-one to the current medical demand emergency level and the corresponding secondary medical demand emergency level of the current medical demand emergency level;

[0186] Further, in order to illustrate the obtaining process of the N secondary emergency medical strategy schemes, an example is given, including:

[0187] Assuming that the current type of public health event or disaster event corresponds to four medical demand emergency levels and the larger the order is, the higher the level is, and the current type of public health event or disaster event is the third level of medical demand emergency, then the third, second and first levels of medical demand emergency are obtained through the above process. The emergency medical strategy scheme corresponding to the normal medical strategy scheme of the current type of public health event or disaster event is obtained.

[0188] The third level of medical demand emergency is fed back to the corresponding health emergency response sub-module or disaster emergency response sub-module, and the second and first levels of medical demand emergency are obtained. The emergency medical strategy scheme and the normal medical strategy scheme corresponding to the current type of public health event or disaster event are saved in the front storage node.

[0189] The first emergency medical strategy scheme that meets the condition is fed back to the corresponding health emergency response sub-module or disaster emergency response sub-module, and the N secondary emergency medical strategy schemes and a normal medical strategy scheme are saved in the front storage node.

[0190] The medical severity level and medical demand emergency level corresponding to the health emergency response sub-module or disaster emergency response sub-module configured with the first emergency medical strategy scheme are obtained in real time. When the real-time medical demand emergency level of the current health emergency response sub-module or disaster emergency response sub-module is less than the medical demand emergency level corresponding to the built-in hierarchical emergency trigger sub-key of the current health emergency response sub-module or disaster emergency response sub-module;

[0191] The degradation trigger mapping is performed through the degradation trigger point configured between the health emergency response sub-module or disaster emergency response sub-module corresponding to the current type of public health event or disaster event.

[0192] At the same time, through the degradation trigger mapping and the first hierarchical emergency strategy search sub-model, the corresponding secondary emergency medical strategy scheme stored in the front storage node is called according to the medical demand emergency level after degradation.

[0193] The emergency medical strategy scheme includes a medical resource distribution configuration sub-strategy of the current region and a missing medical resource rapid configuration and calling sub-strategy corresponding to the current region.

[0194] Further, the medical resource distribution configuration sub-strategy of the current region in the embodiment is an emergency medical strategy scheme for the medical severity level of the current region facing the public health event or disaster event, the corresponding medical demand emergency level and the maximum adjustable and reformed resource configuration of the current region.

[0195] Further, the current region corresponds to the missing medical resource rapid configuration and calling sub-strategy, that is, the pre-dispatching and configuration strategy corresponding to the missing resource amount of the medical resource distribution configuration sub-strategy of the current region is configured, and the missing medical resource is prepared in advance to meet the medical resource and building reconstruction and conversion demand under the medical severity level and the corresponding medical demand emergency level of the corresponding public health event or disaster event.

[0196] The above degradation trigger mapping process is repeated, and the medical severity level corresponding to the health emergency response submodule or disaster emergency response submodule is evaluated in real time;

[0197] When the medical severity level corresponding to the current type of public health event or disaster event is less than the medical severity level threshold of the corresponding type of public health event or disaster event built in the first emergency judgment key, the pre-dispatching and configuration strategy corresponding to the missing resource amount of the medical resource distribution configuration sub-strategy of the current region is configured, and the missing medical resource is prepared in advance to meet the medical resource and building reconstruction and conversion demand under the medical severity level and the corresponding medical demand emergency level of the corresponding public health event or disaster event.

[0198] Here, the normal medical strategy scheme corresponding to the first normal medical strategy scheme or the second normal medical strategy scheme is not called, but the normal medical strategy scheme corresponding to the first emergency medical strategy scheme and the N secondary emergency medical strategy scheme that meets the medical demand emergency level and the compatibility score greater than the compatibility threshold is stored in the pre-storage node.

[0199] This process has many advantages in medical emergency management, such as precise emergency strategy matching, triggering specific trigger mapping relationship and hierarchical emergency trigger sub-key according to the severity of the event, calling the first hierarchical emergency strategy search sub-model, combining the explicit state set, the medical resource distribution state of the corresponding region, and the medical demand emergency level, etc. Information is retrieved in the pre-storage node to match the scheme, and the medical emergency strategy scheme highly compatible with the event demand is ensured by using the medical emergency strategy simulation evolution model built in the sub-module to update the compatibility.

[0200] Second, resource optimization and rapid response, the medical resource distribution configuration sub-strategy of the current region and the missing medical resource rapid configuration and calling sub-strategy corresponding to the current region synergize to reasonably allocate existing resources, prepare missing resources in advance, and quickly meet the emergency medical resource and building reconstruction and conversion demand according to the severity of the event and the medical demand emergency level.

[0201] Third, dynamic adjustment of strategy and flexible response, real-time monitoring of medical demand emergency level change by means of degradation trigger point, once the level is reduced, the degradation trigger mapping is triggered, the corresponding secondary emergency medical strategy scheme is called from the front storage node through the first hierarchical emergency strategy search sub-model, and the strategy is dynamically adjusted with the development of the event.

[0202] Fourth, knowledge reuse and experience accumulation, when the strategy scheme is acquired and updated, the front storage node is searched and stored, and the emergency and normal medical strategy schemes of different types and levels of events are saved, so that similar events can be quickly called when similar events occur in the future, and the emergency response ability is continuously improved.

[0203] The embodiments of the application are described above in combination with the drawings, but the application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative but not restrictive, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments without departing from the purpose of the application and the scope protected by the claims under the inspiration of the application, which are all within the protection of the application.

[0204] If the technical solution of the present disclosure involves personal information, the product applying the technical solution of the present disclosure has been explicitly informed of the personal information processing rules before processing the personal information and has obtained the personal independent consent. If the technical solution of the present disclosure involves sensitive personal information, the product applying the technical solution of the present disclosure has obtained the personal independent consent before processing the sensitive personal information and at the same time meets the requirement of "explicit consent". For example, at the personal information collection device such as camera, a clear and prominent mark is set to inform that the personal information collection range has been entered and the personal information will be collected, and if the individual voluntarily enters the collection range, it is considered to agree to collect the personal information; or on the device for processing personal information, the personal information processing rules are informed by using obvious marks / information, and the personal authorization is obtained by means of pop-up information or asking the individual to upload the personal information; wherein, the personal information processing rules can include personal information processor, personal information processing purpose, processing method and personal information type, etc.

Claims

1. An AI-based adaptive emergency-response method for medical space, characterized in that, include: Acquire information on the severity of healthcare needs, the potential for switching between routine and emergency healthcare needs, and a database of routine and emergency healthcare strategies. Based on the severity of the medical need, the emergency / normal medical transition space configuration key is triggered, and the corresponding emergency / normal medical strategy solution is obtained from the configured emergency / normal medical strategy library. The medical emergency transition space includes an emergency level configuration subspace, an emergency dispatch simulation subspace, and a normalization simulation subspace; The emergency level configuration subspace, emergency dispatch simulation subspace, and normalized simulation subspace are connected in pairs through a normal-emergency conversion discrimination key; The emergency response level configuration subspace is configured with a comprehensive fuzzy evaluation model, which is used to evaluate the input explicit state set and obtain the corresponding severity of medical needs. The emergency level configuration subspace includes a first emergency / normal judgment key, which is used to determine whether to enter an emergency state based on the severity of the medical needs at the current moment. The first emergency / normal judgment key is connected to the corresponding hierarchical emergency trigger subkey of each type of health emergency response module and disaster emergency response module under the emergency dispatch simulation subspace through the configured hierarchical emergency trigger chain; each health emergency response submodule, disaster emergency response submodule and normalization simulation subspace under the emergency dispatch simulation subspace is configured with a medical emergency / normal strategy simulation evolution model, which is used to evaluate and update the medical emergency / normal strategy simulation evolution based on the medical severity level, medical demand emergency level and the medical resource allocation distribution status of the corresponding area for different types of public health events or disaster events; The first emergency / normal discrimination key is connected to the normalized simulation subspace through a configured normal chain; The hierarchical emergency trigger subkeys corresponding to each type of health emergency response module and disaster emergency response module are connected to the normalized simulation subspace through the configured normal-to-emergency conversion subchain set, and each normal-to-emergency conversion subchain is configured with a normal-to-emergency discrimination subkey. The hierarchical emergency trigger subkeys corresponding to each type of health emergency response module or disaster emergency response module are mapped one-to-one with the severity of medical needs of the corresponding type of public health event or disaster event through the configured hierarchical trigger mapping relationship; Each type of health emergency response module or disaster emergency response module is configured with a degraded trigger point between the corresponding hierarchical emergency trigger subkeys. The degradation trigger point is used to assess in real time the severity of medical needs of the public health event or disaster event corresponding to the current type of health emergency response module or disaster emergency response module, and to map the degradation trigger strategy according to the real-time assessment results and the configured hierarchical trigger mapping relationship. Each type of health emergency response module or disaster emergency response module includes the same number of health emergency response sub-modules or disaster emergency response sub-modules as the corresponding hierarchical emergency triggering sub-keys; Each level of emergency triggering subkey corresponds one-to-one with each corresponding health emergency response submodule or disaster emergency response submodule; The corresponding health emergency response sub-modules or disaster emergency response sub-modules under each type of health emergency response module or disaster emergency response module are connected through the degradation trigger point; The medical routine / emergency strategy library is connected to the medical routine / emergency conversion space through a configured dual-level hierarchical path model. The dual-level path model includes a first-level emergency strategy search sub-model and a second-level normal strategy search sub-model. Each of the health emergency response submodules and disaster emergency response submodules is connected to the front-end storage node configured in the medical routine and emergency strategy library through the first hierarchical emergency strategy search sub-model. The normalized simulation subspace is connected to the medical emergency strategy library through the second normalized strategy search sub-model.

2. The AI-based adaptive emergency-response switching method for medical space as described in claim 1, characterized in that, The steps for obtaining the hierarchical trigger mapping relationship include: Obtain the explicit state set and the corresponding medical resource allocation distribution state set corresponding to different types of historical public health events or disaster events; Based on the explicit state set corresponding to the corresponding type of public health event or disaster event, a comprehensive fuzzy evaluation algorithm is used to obtain the severity score of medical needs for the corresponding type of public health event or disaster event. By combining the preset assessment level ranges for public health events or disasters with the severity score of medical needs for the corresponding type of public health event or disaster, the medical severity level of the corresponding type of public health event or disaster can be obtained. Based on the distribution set of medical resource allocation under different levels of medical severity, a score for the emergency response level of medical resources is obtained, and based on the range of medical resource demand levels, the emergency response level of medical needs is obtained.

3. The AI-based adaptive emergency-response switching method for medical space as described in claim 2, characterized in that, The step of obtaining the hierarchical trigger mapping relationship also includes: Set a threshold set for the medical severity level of each type of public health event or disaster event and embed it into the first emergency / routine judgment key; The medical resource demand level range corresponding to the emergency medical demand level is built into the corresponding type of health emergency response submodule or disaster emergency response submodule to construct a hierarchical emergency trigger subkey; Based on the medical severity level discrimination threshold set, the medical severity level of the corresponding type of public health event or disaster event is initially classified to obtain the emergency medical severity level and normal severity level corresponding to each type of public health event or disaster event. Based on the emergency medical severity level and the corresponding emergency medical demand level for each type of public health event or disaster, a one-to-one mapping function between the medical severity level and the emergency medical demand level is established through the support vector machine kernel function to obtain the hierarchical triggering mapping relationship. Similarly, based on the normal severity level and the corresponding emergency medical need level, a normal level mapping relationship is obtained; The hierarchical triggering mapping relationship is mapped to the hierarchical emergency triggering chain, and the normal level mapping relationship is mapped to the normal chain. The medical configuration simulation trigger for the corresponding type of public health event or disaster event is performed to obtain the medical routine and emergency strategy plan corresponding to the corresponding type of public health event or disaster event.

4. The AI-based adaptive emergency-response switching method for medical space as described in claim 3, characterized in that, The steps for obtaining the corresponding emergency medical strategy plan for the corresponding type of public health event or disaster event include: Real-time acquisition of the explicit state set corresponding to the type of public health event or disaster event in the current region and the distribution status of medical resource allocation in the current region; Based on the explicit state set acquired in real time, the severity of medical needs and the level of medical severity corresponding to the current type of public health event or disaster event are obtained through the emergency level configuration subspace. Input the medical severity level corresponding to the current type of public health event or disaster event into the first emergency / normal judgment key; When the medical severity level corresponding to the current type of public health event or disaster event is less than the corresponding medical severity level discrimination threshold, the medical severity level corresponding to the current type of public health event or disaster event is determined to be the normal severity level, and the normal level mapping relationship in the normal chain is triggered.

5. The AI-based adaptive emergency-response switching method for medical space as described in claim 4, characterized in that, The step of obtaining the medical emergency response plan corresponding to the corresponding type of public health event or disaster event also includes: Based on the normal level mapping relationship and the second normal strategy search sub-model between the normalized simulation subspace and the medical routine and emergency strategy library, a search is performed for the medical routine and emergency strategy scheme corresponding to the current normal severity level. The steps for searching for routine and emergency medical strategy plans corresponding to the current severity level include: Based on the current severity level of normal conditions, the corresponding emergency medical need level is obtained through the normal condition level mapping relationship; Based on the emergency medical demand level, the pre-stored nodes in the medical routine emergency strategy library are matched and searched through the second normal strategy search sub-model to determine whether there are medical routine emergency strategy schemes with the same emergency medical demand level as the current type of public health event or disaster event. If so, the corresponding medical emergency strategy will be fed back as the current first normal medical strategy to the medical emergency strategy simulation evolution model configured in the normalized simulation subspace. Simultaneously, by combining the explicit state set corresponding to the current type of public health event or disaster with the distribution of medical resources in the current region, the evolution of the current routine and emergency medical strategy is simulated to obtain the fit score between the first routine medical strategy and the current type of public health event or disaster with the distribution of medical resources in the current region.

6. The AI-based adaptive emergency-response switching method for medical space as described in claim 5, characterized in that, The step of searching for routine and emergency medical strategy plans corresponding to the current severity level also includes: Set a fit threshold. If the fit score of the first normal medical strategy is greater than the fit threshold, then the first normal medical strategy will be configured in the current region as the corresponding medical routine and emergency strategy. If the fit score of the first normal medical strategy is less than or equal to the fit threshold, then the information of the corresponding difference points in the first normal medical strategy is used, combined with the explicit state set corresponding to the current type of public health event or disaster event and the medical resource allocation distribution status corresponding to the current region, and the second normal strategy search sub-model is used to perform conditional mapping search on the medical routine and emergency strategy library to obtain the second normal medical strategy. The second normal medical strategy is fed back to the normalized simulation subspace, and the evolution simulation and fit determination process of the first normal medical strategy is repeated to obtain a normal medical strategy that satisfies the fit score being greater than the fit threshold. If not, the explicit state set corresponding to the current type of public health event or disaster event, the distribution status of medical resource allocation in the current region, and the corresponding emergency level of medical needs are directly used to search from the medical routine and emergency strategy library through the second normal strategy search sub-model. The normalized simulation subspace is combined with the fit threshold to perform cyclical evolution simulation and discrimination to obtain normalized medical strategy schemes that meet the fit score greater than the fit threshold.

7. The AI-based adaptive emergency-response switching method for medical space as described in claim 6, characterized in that, The step of obtaining the medical emergency response plan corresponding to the corresponding type of public health event or disaster event also includes: When the medical severity level corresponding to the current type of public health event or disaster event is greater than or equal to the corresponding medical severity level discrimination threshold, the medical severity level corresponding to the current type of public health event or disaster event is determined to be the emergency medical severity level. Based on the severity level of the emergency medical care, the corresponding graded emergency trigger subkey in the graded emergency trigger chain is embedded with the current type of public health event or disaster event triggering mapping relationship; The corresponding health emergency response submodule or disaster emergency response submodule is triggered according to the current type of public health event or disaster event trigger mapping relationship; The first-level emergency strategy search sub-model is invoked by calling the currently triggered tiered emergency trigger sub-key. This sub-model combines the explicit state set corresponding to the current type of public health event or disaster event with the distribution status of medical resource allocation in the current region, the corresponding emergency level of medical needs, and the secondary emergency level of medical needs corresponding to the current emergency level of medical needs. Through the same process as obtaining the normal medical strategy scheme, the first emergency medical strategy scheme that meets the emergency level of medical needs and has a matching score greater than the matching threshold, along with N corresponding secondary emergency medical strategy schemes and one normal medical strategy scheme, are obtained.

8. The AI-based adaptive emergency-response switching method for medical space as described in claim 7, characterized in that, The step of obtaining the medical emergency response plan corresponding to the corresponding type of public health event or disaster event also includes: The first emergency medical strategy plan that meets the conditions will be fed back to the corresponding health emergency response submodule or disaster emergency response submodule, and at the same time... N One secondary emergency medical strategy plan and one routine medical strategy plan are saved to the aforementioned front-end storage node; Real-time acquisition of the medical severity level and medical demand emergency level corresponding to the health emergency response submodule or disaster emergency response submodule after the first emergency medical strategy plan is configured. When the real-time medical demand emergency level corresponding to the current health emergency response submodule or disaster emergency response submodule is less than the medical demand emergency level corresponding to the built-in graded emergency trigger subkey of the current health emergency response submodule or disaster emergency response submodule; Degradation trigger mapping is performed through the degradation trigger points configured between the health emergency response submodules or disaster emergency response submodules corresponding to the current type of public health event or disaster event; Simultaneously, by using the downgrade trigger mapping and the first-level emergency strategy search sub-model, the corresponding secondary emergency medical strategy scheme stored in the front-end storage node is invoked according to the emergency level of the medical needs after downgrade. The emergency medical strategy includes a sub-strategy for the distribution and configuration of medical resources in the current area and a sub-strategy for the rapid configuration and mobilization of missing medical resources in the current area. Repeat the downgrade trigger mapping process and assess the medical severity level corresponding to the health emergency response submodule or disaster emergency response submodule in real time; When the medical severity level corresponding to the current type of public health event or disaster event is less than the medical severity level discrimination threshold of the corresponding type of public health event or disaster event built into the first emergency / normal discrimination key; By utilizing the emergency / normal transition sub-key in the emergency / normal transition sub-chain between the current health emergency response sub-module or disaster emergency response sub-module and the normalized simulation subspace, the normalized medical strategy scheme corresponding to the current type of public health event or disaster event stored in the front-end storage node is invoked through the second normalized strategy search sub-model.

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