Stroke emergency map system based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based stroke first-aid map system, which comprises a data acquisition and preprocessing module, an intelligent stroke identification module, an intelligent first-aid resource scheduling module, an intelligent path planning module, a remote consultation and guidance module, an intelligent first-aid quality evaluation module, an iterative optimization module, and real-time data analysis and intelligent navigation. Intelligent route recommendation can help first-aid personnel to quickly determine an optimal route, shorten response time and improve first-aid efficiency; intelligent analysis is carried out on first-aid data by using an artificial intelligence algorithm, pre-hospital intelligent early warning is realized, intelligent identification and auxiliary diagnosis are carried out according to the language expression, the facial form and the states of two arms, two legs and the like of a patient, intelligent auxiliary decision support is provided for first-aid personnel, and the accuracy of first-aid decision is improved; first-aid resources are optimized and intelligently scheduled through an intelligent algorithm, it is ensured that patients are sent to appropriate hospitals at correct time, and the first-aid efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical technology, and particularly to a stroke emergency rescue map system based on artificial intelligence. Background Art

[0002] A stroke emergency rescue map is an information-based tool that uses geographic information system technology to provide emergency rescue services for stroke patients. It integrates geographical locations, medical resources, and patient information related to stroke emergency rescue, and provides comprehensive, efficient, and accurate command and dispatch services for the emergency rescue process of stroke patients. The intelligent-assisted decision-making stroke emergency rescue map based on artificial intelligence technology is an innovative patented technology, aiming to optimize the entire process of stroke emergency rescue by using artificial intelligence algorithms and big data analysis, improve the timely treatment rate of stroke patients, and reduce the delay to the hospital.

[0003] Currently, the following problems exist in the traditional stroke emergency rescue map:

[0004] 1) The path planning is not intelligent: The path planning function of the traditional emergency rescue map is relatively simple, and fails to fully consider factors such as real-time road conditions, weather, and vehicle characteristics, resulting in low driving efficiency of emergency rescue vehicles;

[0005] 2) The connection between pre-hospital and in-hospital is not smooth: The information sharing and business collaboration mechanism between pre-hospital emergency rescue and in-hospital treatment is not perfect, affecting the continuity and integration of the emergency rescue process;

[0006] 3) The data utilization is insufficient: The data collected by the emergency rescue map is not fully utilized, lacking in-depth analysis and mining of the data, and it is difficult to play the value of the data in stroke prevention and control. Summary of the Invention

[0007] The purpose of the present invention is to solve the shortcomings existing in the prior art, and to propose a stroke emergency rescue map based on artificial intelligence.

[0008] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0009] A stroke emergency rescue map based on artificial intelligence includes a data collection and preprocessing module, which docks with hospital information systems, emergency dispatch systems, electronic medical record systems, etc., to collect information such as physiological data, symptoms, and electronic medical records of stroke patients in real time, and performs preprocessing such as data cleaning and standardization to prepare for subsequent analysis;

[0010] A stroke intelligent recognition module, which uses machine learning algorithms to analyze information such as the symptoms and signs of patients to achieve early recognition and grading warning of strokes;

[0011] The intelligent dispatching module for first-aid resources uses algorithms such as deep learning to establish an optimization model for first-aid resource dispatching. According to factors such as the patient's location, the severity of the condition, and traffic conditions, it calculates the optimal first-aid route and the target hospital in real time;

[0012] The intelligent route planning module uses intelligent optimization algorithms to plan the optimal first-aid route in real time and dynamically adjusts it according to changes in road conditions;

[0013] The remote consultation and guidance module uses technologies such as 5G, AR / VR, etc. to establish a remote consultation platform, realizing real-time voice and video communication and data sharing between pre-hospital first-aid and in-hospital experts, and providing remote guidance and support for on-site first-aid;

[0014] The intelligent evaluation module for first-aid quality uses machine learning algorithms to mine and analyze the data of the entire first-aid process, evaluate the quality and effect of first-aid, and identify deficiencies in the first-aid process;

[0015] The iterative optimization module uses artificial intelligence technology to continuously monitor and evaluate the stroke first-aid map system, and optimizes the algorithm model through data feedback and learning.

[0016] Preferably, the data acquisition and preprocessing module includes data acquisition, data cleaning, duplicate data removal, data standardization, data encryption, access control, and anonymization processing; the data acquisition collects data from hospital information systems, first-aid dispatching systems, electronic medical record systems, sensors and wearable devices, traffic information systems, geographic information systems, and meteorological information systems.

[0017] Preferably, the data cleaning includes:

[0018] Missing value processing, using interpolation or mean value to fill in the missing data;

[0019] Outlier detection, using statistical methods or machine learning and deep learning algorithms to identify and process abnormal data.

[0020] The duplicate data removal includes removing duplicate data through hashing or other algorithms.

[0021] Preferably, the data standardization includes:

[0022] Format unification, unifying the data formats from different sources into a standard format for structured processing;

[0023] Data normalization, scaling the data to a specific range (such as 0 to 1) to eliminate the influence of dimension.

[0024] The data encryption includes data desensitization, or using encryption algorithms such as AES, RSA, etc. to encrypt and store and transmit sensitive data.

[0025] Preferably, the access control includes adopting privacy computing technologies, as well as measures such as identity authentication and permission management to ensure that only authorized personnel can access sensitive data.

[0026] Preferably, the hospital information system includes basic information of patients, medical records, diagnosis results, etc., as well as basic information and symptoms of patients collected by first-aid personnel on-site; the emergency dispatch system includes information such as the location of emergency vehicles, medical resources, and dispatch instructions; the electronic medical record system includes detailed patient medical histories, treatment plans, etc.; the sensors and wearable devices include physiological parameters of patients, such as heart rate, blood pressure, blood oxygen saturation, etc.; the traffic information system includes real-time traffic conditions, road congestion, traffic accident information, etc.; the geographic information system includes the location of hospitals; the meteorological information system includes weather conditions.

[0027] Preferably, the reinforcement learning algorithm includes:

[0028] Environment definition, state space: including magnetic resonance imaging and ultrasonic imaging features; combined with the patient's clinical information (such as medical history and physical examination information); action space: executable diagnostic and treatment decisions; reward function: positive reward, accurately diagnosing the type of stroke, classifying the stroke grade, and improving the patient's prognosis; negative reward, misdiagnosis, delayed treatment;

[0029] Data preparation, image preprocessing: denoising and normalizing magnetic resonance and ultrasonic images, and using networks such as 3DU-Net for lesion segmentation;

[0030] Feature extraction: extracting key features from images, such as lesion size and location, and using convolutional neural networks (CNNs) to extract deep features;

[0031] Model design, Deep Q-Network (DQN): using CNN to process high-dimensional image data and output the Q values of each possible action; policy gradient methods (such as A3C): dealing with continuous action spaces and being suitable for more complex decisions;

[0032] Training process, simulation environment: creating a virtual environment based on historical data to simulate different diagnostic and treatment paths; experience replay: storing and utilizing past experiences to stabilize the training process; reward optimization: balancing diagnostic accuracy and treatment effects by adjusting the reward mechanism;

[0033] Real-time application, real-time image input: processing new magnetic resonance and ultrasonic images and extracting real-time features; decision output: based on the current state, outputting optimized diagnostic and treatment suggestions; feedback mechanism: updating model parameters in real-time and adjusting according to the actual treatment effects; multi-modal fusion: combining other data sources (such as genomics data, cytomics data, electronic health records) to improve accuracy.

[0034] Preferably, the machine learning algorithm includes one or more of the 3D U-Net algorithm, the convolutional neural network algorithm, and the long short-term memory network algorithm; the 3D U-Net algorithm realizes pixel-level segmentation annotation of medical images through an encoder-decoder structure, and the convolutional neural network algorithm is used for medical image classification to identify stroke-related lesion areas; the long short-term memory network algorithm is used for time series data analysis to analyze the time changes of stroke patients' symptoms; the 3D U-Net algorithm includes data preparation, collecting and preprocessing 3D medical image data; network structure: an encoder that gradually downsamples the input image to extract fine features of the image; a decoder that gradually upsamples to restore the image size and simultaneously fuses the features of the encoder; training, using the cross-entropy loss or Dice loss function, and the optimizer uses Adam or SGD; prediction, inputting the collected 3D medical image and outputting the segmentation result; post-processing, extracting the region of interest based on the segmentation result for further analysis.

[0035] Preferably, the convolutional neural network algorithm includes data preparation, collecting and annotating the image data of stroke patients, accurately annotating by senior physicians, cross-checking the quality, generating a high-quality data training set for model training; network design, constructing multiple convolutional layers, pooling layers, and fully connected layers; training, using the annotated data for supervised learning and applying reinforcement learning and semi-supervised learning.

[0036] Preferably, the long short-term memory network algorithm includes data preparation, collecting time series data (such as the vital signs of patients); network design, constructing an LSTM layer to process time series information; training, using the time series data for training; prediction, inputting new time series data and outputting a stroke risk prediction.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The present invention designs a stroke first aid map based on artificial intelligence. Through intelligent algorithms and big data analysis, it can quickly determine the optimal rescue route, avoiding detours and traffic congestion of first aid vehicles in complex road conditions. It dynamically adjusts the route according to real-time traffic data, effectively coping with traffic congestion, traffic control and other situations; uses machine learning and deep learning algorithms to intelligently analyze the patient's condition data, providing auxiliary diagnosis and decision-making support for first aid personnel. Through intelligent scheduling algorithms, considering factors such as the location of first aid vehicles, hospital beds, equipment, medicines and technologies, it realizes the optimal allocation of first aid resources. According to factors such as the patient's condition and distance, it intelligently recommends the most suitable target hospital, achieving precise diversion of patients. It collects and accumulates a large amount of stroke first aid data, including patient electronic medical records, first aid processes, treatment results, etc. Using big data analysis technology, it mines the correlation rules and hidden patterns in the first aid data to form a reference for decision-making. Based on big data research, it can continuously optimize the first aid process and plan, improving the overall effect of stroke first aid. Through 5G and VR technologies, it realizes close communication between pre-hospital first aid and in-hospital treatment, with experts providing real-time remote guidance. The high bandwidth and low latency characteristics of 5G technology support the real-time transmission of information during the first aid process, while VR technology enables first aid personnel to obtain real-time guidance from experts on-site. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, where:

[0040] Figure 1 is a schematic block diagram proposed by the present invention;

[0041] Figure 2 is a block diagram of data collection and preprocessing proposed by the present invention;

[0042] Figure 3 is a flow block diagram of the 3D U-Net algorithm proposed by the present invention;

[0043] Figure 4 is a flow block diagram of the convolutional neural network algorithm proposed by the present invention;

[0044] Figure 5 is a flow block diagram of the long short-term memory network algorithm proposed by the present invention;

[0045] Figure 6 is a flow block diagram of the reinforcement learning algorithm proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention. In addition, the features defined as "first", "second", and "third" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more; in addition, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0048] Refer to Figures 1 to 6 As shown, an artificial intelligence-based stroke first aid map includes a data collection and preprocessing module. By docking with hospital information systems, emergency dispatch systems, electronic medical record systems, etc., it can collect information such as the physiological data, symptoms, and electronic medical records of stroke patients in real time, and perform preprocessing such as data cleaning and standardization to prepare for subsequent analysis. It adopts an open system architecture and standard interfaces, and can be seamlessly integrated with multiple platforms such as hospital information systems (HIS), emergency command and dispatch systems, and in-vehicle terminals. It supports multiple terminal forms, such as Web terminals, mobile APPs, in-vehicle devices, mini-programs, etc., to meet the usage requirements in different scenarios. It realizes real-time transmission and sharing of data among various platforms, improving the informatization level and collaborative efficiency of each link in first aid.

[0049] The stroke intelligent recognition module uses machine learning algorithms to analyze information such as the symptoms and signs of patients, realizing early recognition and grading warning of strokes; constructs a dataset of clinical manifestations and various parameter indicators related to stroke for training the stroke auxiliary diagnosis model. During the process of the ambulance going to the hospital, parameters such as the patient's heart rate and blood pressure can be analyzed to judge the severity of the stroke, providing a basis for subsequent treatment and diagnosis. Utilize artificial intelligence technologies such as machine learning, deep learning, and large language models to train and learn from the massive data related to stroke first aid. Through intelligent algorithms, real-time analysis and processing of multi-source heterogeneous data such as the patient's condition, traffic conditions, weather conditions, and hospital resources are carried out. Provide intelligent decision-making support for first aid personnel, such as intelligent recommendation of the optimal rescue route and the best hospital recommendation, etc.

[0050] The first aid resource intelligent scheduling module uses algorithms such as deep learning to establish an optimization model for first aid resource scheduling. According to factors such as the patient's location, the severity of the condition, and traffic conditions, it calculates the optimal first aid path and the target hospital in real time; uses machine learning and deep learning algorithms to intelligently analyze data such as the patient's condition and vital signs, providing auxiliary diagnosis and decision-making support for first aid personnel. Combining a massive medical knowledge base and expert experience, it provides personalized first aid plan recommendations, reducing the risk of misdiagnosis and missed diagnosis.

[0051] The intelligent path planning module uses intelligent optimization algorithms to plan the optimal first aid path in real time and dynamically adjusts it according to changes in road conditions; through intelligent algorithms and big data analysis, it quickly determines the optimal rescue route, avoiding detours and traffic jams of first aid vehicles in complex road conditions. Dynamically adjusts the route according to real-time traffic data, effectively coping with situations such as traffic jams and traffic control. Adopts real-time data collection and transmission technology to real-time track and update information such as the GPS positioning of first aid vehicles, traffic conditions, weather conditions, and hospital beds. According to the changes in real-time data, dynamically adjusts first aid strategies and routes to ensure the timeliness and accuracy of the rescue plan. Realizes real-time dynamic optimization of the entire process from call to arrival and from the scene to the hospital, minimizing the first aid time to the greatest extent. Adopts a high-precision electronic map as the underlying support to provide accurate location services and path planning. The map data includes multi-dimensional information such as road networks, address points, and points of interest. Key information such as hospital grades, hospital specialty diagnosis and treatment levels, and hospital beds are marked on the map to form a dataset, training the algorithm model to develop an intelligent stroke first aid map, building a reliable data foundation and intelligent engine for intelligent first aid recommendation and navigation.

[0052] Remote consultation and guidance module: Using technologies such as 5G, AR / VR, etc., a remote consultation platform is established to achieve real-time voice and video communication and data sharing between pre-hospital first aid and in-hospital experts, providing remote guidance and support for on-site first aid; through 5G and VR technologies, close communication between pre-hospital first aid and in-hospital treatment is achieved, and experts provide real-time remote guidance. The high bandwidth and low latency characteristics of 5G technology support the real-time transmission of information during the first aid process, while VR technology enables first aid personnel to obtain real-time guidance from experts on-site.

[0053] Intelligent evaluation module for first aid quality: Using machine learning algorithms, mine and analyze the data of the entire first aid process, evaluate the quality and effect of first aid, and identify deficiencies in the first aid process; adopt a human-machine collaborative interaction method to give full play to the respective advantages of artificial intelligence and human experience. It can dynamically optimize and adjust the rescue plan according to the input and feedback of first aid personnel, realizing human-machine intelligent interaction and collaborative decision-making. At the same time, a friendly visualization interface is provided to display the rescue process and key information in a graphical and visual way, facilitating the understanding and operation of first aid personnel.

[0054] Iterative optimization module: Using artificial intelligence technology, continuously monitor and evaluate the stroke first aid map system, and optimize the algorithm model through data feedback and learning; collect and accumulate a large amount of stroke first aid data, including patient electronic medical records, first aid processes, treatment results, etc. Using big data analysis technology, mine the correlation laws and hidden patterns in the first aid data to form a reference for decision-making. Based on big data research, continuously optimize the first aid process and plan to improve the overall effect of stroke first aid. By collecting and analyzing a large amount of first aid case data, continuously improve and optimize the intelligent algorithm model. Using technologies such as deep learning, enable the system to continuously self-adjust and improve according to new data and feedback, achieving continuous optimization of performance. Through continuous iterative optimization, continuously improve the intelligent level and practical application effect of the system, providing long-term technical support for stroke first aid.

[0055] The data collection and preprocessing module includes data collection, data cleaning, duplicate data removal, data standardization, data encryption, access control, and anonymization processing; data collection collects data from hospital information systems, emergency dispatch systems, electronic medical record systems, sensors and wearable devices, traffic information systems, geographic information systems, and meteorological information systems.

[0056] Data cleaning includes: missing value handling, using interpolation or mean value to fill in missing data; outlier detection, using statistical methods or machine learning and deep learning algorithms to identify and process abnormal data. Duplicate data removal includes removing duplicate data through hashing or other algorithms. Data standardization includes: format unification, unifying the data formats from different sources into a standard format and performing structured processing; data normalization, scaling the data to a specific range (such as 0 to 1) to eliminate the influence of dimensionality. Data encryption includes data desensitization, or using encryption algorithms such as AES and RSA to encrypt and store sensitive data during transmission. Access control includes using privacy computing technologies, as well as measures such as authentication and permission management to ensure that only authorized personnel can access sensitive data.

[0057] The hospital information system includes patients' basic information, medical records, diagnosis results, etc., as well as the basic information and symptoms of patients collected by emergency personnel on-site; the emergency dispatch system includes information such as the location of emergency vehicles, medical resources, and dispatch instructions; the electronic medical record system includes detailed patient medical histories, treatment plans, etc.; sensors and wearable devices include patients' physiological parameters, such as heart rate, blood pressure, and blood oxygen saturation; the traffic information system includes real-time traffic conditions (such as road congestion, traffic accidents); the geographic information system includes the location of hospitals; the meteorological information system includes weather conditions.

[0058] Reinforcement learning algorithms include: environment definition, state space: including magnetic resonance images and ultrasonic image features; combining patients' clinical information (such as medical history and physical examination information); action space: executable diagnostic and treatment decisions; reward function: positive reward, accurately diagnosing the type and grade of stroke and improving the patient's prognosis; negative reward, misdiagnosis and delayed treatment; data preparation, image preprocessing: denoising and normalizing magnetic resonance and ultrasonic images, using networks such as 3D U-Net for lesion segmentation; feature extraction: extracting key features from images, such as lesion size and location, and using convolutional neural networks (CNNs) to extract deep features; model design, deep Q network (DQN): using CNNs to process high-dimensional image data and output the Q values of each possible action; policy gradient methods (such as A3C): dealing with continuous action spaces and being suitable for more complex decisions; training process, simulation environment: creating a virtual environment based on historical data to simulate different diagnostic and treatment paths; experience replay: storing and utilizing past experiences to stabilize the training process; reward optimization: balancing diagnostic accuracy and treatment effect by adjusting the reward mechanism; real-time application, real-time image input: processing new magnetic resonance and ultrasonic images and extracting real-time features; decision output: based on the current state, outputting optimized diagnostic and treatment suggestions; feedback mechanism: updating model parameters in real time and adjusting according to the actual treatment effect; multi-modal fusion: combining other data sources (such as genomics data, cellomics data, and electronic health records) to improve accuracy.

[0059] The machine learning algorithms include one or more of the 3D U-Net algorithm, the convolutional neural network algorithm, and the long short-term memory network algorithm; the 3D U-Net algorithm realizes pixel-level segmentation and annotation of medical images through an encoder-decoder structure, the convolutional neural network algorithm is used for medical image classification to identify stroke-related lesion areas; the long short-term memory network algorithm is used for time series data analysis to analyze the temporal changes in the symptoms of stroke patients; the 3D U-Net algorithm includes data preparation, collecting and preprocessing 3D medical image data; network structure: an encoder that gradually downsamples the input image to extract fine features of the image; a decoder that gradually upsamples to restore the image size while fusing the features of the encoder; training, using the cross-entropy loss or Dice loss function, and the optimizer uses Adam or SGD; prediction, inputting the collected 3D medical image and outputting the segmentation result; post-processing, extracting the region of interest based on the segmentation result for further analysis.

[0060] The convolutional neural network algorithm includes data preparation, collecting and annotating the image data of stroke patients, accurately annotating by senior physicians, cross-checking, and generating a high-quality data training set for model training; network design, constructing multiple convolutional layers, pooling layers, and fully connected layers; training, using the labeled data for supervised learning and applying reinforcement learning and semi-supervised learning.

[0061] The long short-term memory network algorithm includes data preparation, collecting time series data (such as the vital signs of patients); network design, constructing an LSTM layer to process time series information; training, using the time series data for training; prediction, inputting new time series data and outputting the stroke risk prediction.

[0062] It has significant advantages in shortening the response time, improving decision-making accuracy, optimizing resource scheduling, accumulating application big data, etc., realizing pre-hospital intelligent identification and diagnosis, "getting in the car and going to the hospital", connecting the regional hospitals and the regional stroke center in series for linkage, real-time dynamic monitoring, "green channel referral", being able to comprehensively improve the efficiency and effect of stroke first aid, providing strong guarantee for the rescue and treatment of stroke patients, and having broad application prospects and social value.

[0063] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0064] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. A stroke emergency map system based on artificial intelligence, characterized in that: It includes a data collection and preprocessing module, which connects with the hospital information system, emergency dispatch system, electronic medical record system, etc. to collect the physiological data, symptoms, electronic medical records and other information of stroke patients in real time, and performs preprocessing such as data cleaning and standardization to prepare for subsequent analysis; The stroke intelligent identification module uses machine learning algorithms to analyze the patient's symptoms, signs and other information to achieve early identification and warning of stroke; The intelligent dispatching module for emergency resources uses deep learning and other algorithms to establish an optimization model for dispatching emergency resources. It calculates the optimal emergency route and target hospital in real time based on factors such as patient location, severity of illness, and traffic conditions. Intelligent route planning module, which uses intelligent optimization algorithms to plan the optimal emergency route in real time and dynamically adjusts it according to changes in road conditions; Remote consultation and guidance module, using 5G, AR / VR and other technologies to establish a remote consultation platform, realize real-time voice and video communication and data sharing between pre-hospital emergency and in-hospital experts, and provide remote guidance and support for on-site emergency; The first aid quality intelligent assessment module uses machine learning algorithms to mine and analyze data from the entire first aid process, evaluate the quality and effectiveness of first aid, and identify deficiencies in the first aid process; The iterative optimization module uses artificial intelligence technology to continuously monitor and evaluate the stroke emergency map system, and optimizes the algorithm model through data feedback and learning.

2. According to claim 1, the stroke emergency map system based on artificial intelligence is characterized in that: The data collection and preprocessing module includes data collection, data cleaning, duplicate data removal, data standardization, data encryption, access control and anonymization processing; the data collection collects data from hospital information systems, emergency dispatch systems, electronic medical record systems, sensors and wearable devices, traffic information systems, geographic information systems and meteorological information systems.

3. According to claim 2, the stroke emergency map system based on artificial intelligence is characterized in that: The data cleaning includes: Missing value handling, using interpolation or mean value to fill missing data; Outlier detection uses machine learning and deep learning algorithms to identify and process abnormal data. The deduplication of data includes deduplication of data by using a hash algorithm or other algorithms.

4. According to claim 2, the stroke emergency map system based on artificial intelligence is characterized in that: The data standardization includes: Format unification: unify data formats from different sources into a standard format and perform structured processing; Data normalization is the process of scaling the data to a specific range (such as 0 to 1) to eliminate the effects of dimensioning. The data encryption includes data desensitization, or using encryption algorithms such as AES and RSA to encrypt, store and transmit sensitive data.

5. The stroke emergency map system based on artificial intelligence according to claim 4 is characterized in that: The access control includes the use of privacy computing technology, as well as identity authentication, permission management and other measures to ensure that only authorized personnel can access sensitive data. The anonymization process includes anonymizing patient data to prevent leakage of personal identity information.

6. The stroke emergency map system based on artificial intelligence according to claim 5, characterized in that: The hospital information system includes the patient's basic information, medical history, diagnosis results, etc., as well as the patient's basic information and symptoms collected on-site by emergency personnel; the emergency dispatch system includes information such as the location of emergency vehicles, medical resources, dispatch instructions, etc.; the electronic medical record system includes detailed patient medical history, treatment plans, etc.; the sensors and wearable devices include the patient's physiological parameters, such as heart rate, blood pressure, blood oxygen saturation, etc.; the traffic information system includes real-time traffic conditions, road congestion, traffic accident information, etc.; the geographic information system includes the location of the hospital; the meteorological information system includes weather conditions.

7. The stroke emergency map system based on artificial intelligence according to claim 1, characterized in that: The reinforcement learning algorithm includes: Environment definition, state space: including magnetic resonance imaging and ultrasound imaging features; combined with the patient's clinical information (such as medical history and physical examination information); action space: executable diagnosis and treatment decisions; reward function: positive rewards, accurate diagnosis of stroke type, stroke grade classification, and improved patient prognosis; negative rewards, misdiagnosis and delayed treatment; Data preparation, image preprocessing: denoising and standardizing magnetic resonance and ultrasound images, and using networks such as 3DU-Net for lesion segmentation; Feature extraction: Extract key features from images, such as lesion size and location, and use convolutional neural networks (CNNs) to extract deep features; Model design, Deep Q Network (DQN): Use CNN to process high-dimensional image data and output the Q value of each possible action; Policy Gradient Method (such as A3C): Process continuous action space and is suitable for more complex decision-making; Training process, simulation environment: Create a virtual environment based on historical data to simulate different diagnostic and treatment paths; Experience replay: Store and use past experience to stabilize the training process; Reward optimization: Balance diagnostic accuracy and treatment effectiveness by adjusting the reward mechanism; Real-time application, real-time image input: process new magnetic resonance and ultrasound images and extract real-time features; decision output: output optimized diagnosis and treatment recommendations based on the current status; feedback mechanism: update model parameters in real time and adjust according to actual treatment effects; multimodal fusion: combine other data sources (such as genomic data, cytomics data, electronic health records) to improve accuracy.

8. The stroke emergency map system based on artificial intelligence according to claim 1, characterized in that: The machine learning algorithm includes one or more of a 3D U-Net algorithm, a convolutional neural network algorithm, and a long short-term memory network algorithm; the 3D U-Net algorithm implements pixel-level segmentation and annotation of medical images through an encoder-decoder structure, and the convolutional neural network algorithm is used for medical image classification to identify stroke-related lesion areas; the long short-term memory network algorithm is used for time series data analysis to analyze the temporal changes in symptoms of stroke patients; the 3D U-Net algorithm includes data preparation, collecting and preprocessing 3D medical image data; network structure: encoder, gradually downsampling input images to extract subtle image features; decoder, gradually upsampling to restore image size and fusing encoder features at the same time; training, using cross entropy loss or Dice loss function, and optimizer using Adam or SGD; prediction, inputting the collected 3D medical images and outputting segmentation results; post-processing, extracting regions of interest based on the segmentation results for further analysis.

9. The stroke emergency map system based on artificial intelligence according to claim 8, characterized in that: The convolutional neural network algorithm includes data preparation, collecting and labeling imaging data of stroke patients, accurate labeling by senior physicians, cross-checking, and generating high-quality data training sets for model training; network design, constructing multi-layer convolutional layers, pooling layers, and fully connected layers; Training, supervised learning using labeled data, and reinforcement learning, semi-supervised learning.

10. The stroke emergency map system based on artificial intelligence according to claim 9, characterized in that: The LSTM network algorithm includes data preparation, collecting time series data (such as patients' vital signs); network design, building LSTM layers, and processing time series information; Training, using time series data for training; Prediction, input new time series data, output stroke risk prediction.

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