Emergency medical command method and system based on AI and Internet of Things

Abstract

The invention provides an emergency medical command method and system based on AI and Internet of Things, and relates to the technical field of emergency medical command, and the method comprises the steps: collecting thermal imaging data through an infrared sensor, obtaining the body temperature and position information of a wounded person, calculating the regional danger level through combining with environment parameters, and generating a scene situation evaluation result. And dividing rescue areas and formulating a resource scheduling scheme based on an evaluation result, visually presenting the scheme on a digital twin platform, and superposing a rescue situation through an augmented reality technology. And dynamically adjusting resource configuration according to the real-time position information. According to the invention, intelligent medical command on an emergency site can be realized, and the rescue efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to emergency medical command technology, and in particular to an emergency medical command method and system for emergencies based on AI and the Internet of Things. Background Art

[0002] In emergencies, the timeliness and accuracy of emergency medical rescue are crucial. The existing emergency medical command systems have the following deficiencies: First, the early warning and classification of emergencies are not accurate enough to quickly judge the danger level of the event; second, the resource scheduling efficiency is low, and cross-regional and multi-departmental collaborative linkage cannot be achieved; third, there is a lack of intelligent decision-making support and the rescue plan cannot be dynamically adjusted according to real-time data.

[0003] With the development of artificial intelligence and Internet of Things technologies, it has become possible to apply technologies such as intelligent perception and digital twin to emergency medical command. However, there is still a lack of a comprehensive solution that can realize real-time monitoring of the wounded's condition, intelligent assessment of the on-site situation, and optimized scheduling of medical resources. Therefore, there is an urgent need for an emergency medical command method for emergencies based on AI and the Internet of Things to improve the scientific nature and efficiency of emergency medical rescue. Summary of the Invention

[0004] Embodiments of the present invention provide an emergency medical command method and system for emergencies based on AI and the Internet of Things, which can solve the problems in the prior art.

[0005] In the first aspect of the embodiments of the present invention, An emergency medical command method for emergencies based on AI and the Internet of Things is provided, including: Collecting thermal imaging data of the emergency site through an infrared sensor, extracting the body temperature distribution data and the location data of the wounded according to the thermal imaging data, determining the environmental monitoring range according to the location data of the wounded, and collecting environmental parameter data through an environmental sensor within the range; Constructing a body temperature change trend chart according to the body temperature distribution data of the wounded, calculating the value of the abnormal body temperature degree, and at the same time calculating the value of the environmental threat degree according to the environmental parameter data, calculating the regional danger level based on the value of the abnormal body temperature degree and the value of the environmental threat degree, and counting the wounded distribution density in combination with the location data of the wounded, and generating a scene situation assessment result according to the regional danger level and the wounded distribution density; Based on the scene situation assessment result, dividing the rescue area according to the wounded distribution density, and determining the resource allocation weight of each rescue area based on the regional danger level, and generating a medical resource scheduling plan; Build a 3D model of the emergency site in the digital twin platform, visually map the scene situation assessment results and the medical resource scheduling plan, overlay the rescue situation annotations in real time through augmented reality technology, and update the location information of rescue personnel and medical equipment in real time based on the target tracking algorithm; According to the location information of rescue personnel and medical equipment, dynamically adjust the medical resource scheduling plan through a recursive optimization algorithm to achieve real-time optimal allocation of medical resources.

[0006] In an alternative embodiment, Collect the thermal imaging data of the emergency site through an infrared sensor, and extract the casualty body temperature distribution data and casualty location data from the thermal imaging data, including: Collect the thermal imaging data of the emergency site through an infrared sensor, perform adaptive histogram equalization enhancement processing on the thermal imaging data to obtain enhanced thermal imaging data, determine the filter window size based on the local temperature variance of the enhanced thermal imaging data, and use the adaptive median filter algorithm to denoise the enhanced thermal imaging data to obtain denoised thermal imaging data; Establish a mapping function between the gray value and the actual temperature according to the temperature response characteristics of the infrared sensor, convert the gray value in the denoised thermal imaging data into a temperature value to generate a temperature distribution map, set a double-threshold detection interval based on the normal body temperature range of the human body, and perform regional connectivity analysis on the pixel points whose temperature values in the temperature distribution map fall within the double-threshold detection interval to obtain the candidate casualty area; Perform morphological processing on the candidate casualty area, extract the region contour features, area features and aspect ratio features, and screen out the areas that do not conform to the features according to the human body size feature template to obtain the confirmed casualty area; Establish a temperature field distribution model for the confirmed casualty area, extract the temperature value sequence, temperature gradient distribution and temperature uniformity features in the area as the casualty body temperature distribution data; calculate the minimum bounding rectangle according to the contour features of the confirmed casualty area, and use the center point coordinates of the rectangle as the casualty location data.

[0007] In an alternative embodiment, Construct a body temperature change trend graph based on the casualty body temperature distribution data, calculate the body temperature abnormality degree value, and at the same time calculate the environmental threat degree value according to the environmental parameter data, calculate the regional risk level based on the body temperature abnormality degree value and the environmental threat degree value, combine the casualty location data to count the casualty distribution density, and generate the scene situation assessment result according to the regional risk level and the casualty distribution density, including: A body temperature monitoring window is established, and the body temperature monitoring window is divided into an observation interval and a prediction interval. The body temperature data in the observation interval is segmented and fitted to extract the fluctuation law. A body temperature baseline is established based on the prediction interval, and the degree of deviation of the fluctuation law from the body temperature baseline is calculated to obtain the fluctuation anomaly feature. The body temperature anomaly degree value is judged according to the fluctuation anomaly feature; An influence network including environmental parameters is constructed according to the environmental parameter data. The leading abnormal parameter is used as the root node, and the affected parameter is used as the child node. A directed graph structure of the transfer relationship between parameters is established. Based on the directed graph structure, the abnormal diffusion path is analyzed, and the environmental threat degree value is calculated by combining the scope and speed of the abnormal diffusion; According to the change trends of the body temperature anomaly degree value and the environmental threat degree value, the inference rules of the neural network are dynamically updated, and the regional danger level is calculated by using the inference rules; Spatial distribution analysis is performed on the casualty location data, a casualty movement trajectory map is established, the trajectory intersection points are extracted as key nodes, and the degree of personnel aggregation at each key node is calculated to obtain the casualty distribution density; Taking the regional danger level as the weight coefficient, the casualty distribution density is weighted and calculated to obtain the scene situation assessment result.

[0008] In an optional embodiment, Spatial distribution analysis is performed on the casualty location data, a casualty movement trajectory map is established, the trajectory intersection points are extracted as key nodes, and the calculation of the degree of personnel aggregation at each key node to obtain the casualty distribution density includes: The casualty location data is segmented according to the time sequence to obtain a time window sequence. The change of the position coordinates is calculated within the time window sequence to obtain a displacement vector. The displacement vectors are connected in sequence to construct a casualty movement trajectory map. The casualty movement trajectory map is smoothed to eliminate abnormal jumps, and the target movement path is extracted according to the smoothed trajectory; The target movement path is drawn in the spatial coordinate system, the intersection angle and the shortest distance between the paths are calculated. When the intersection angle and the shortest distance meet the preset conditions, the trajectory intersection points are determined. The intersection frequency at each trajectory intersection point is counted, and the number of personnel within the unit area around the trajectory intersection point is calculated to obtain the degree of personnel aggregation. The key nodes are determined according to the weighted values of the intersection frequency and the degree of personnel aggregation; A local coordinate system is established with the key node as the center, the intersection frequency and the degree of personnel aggregation are respectively mapped to the size of the influence range and the attenuation rate, a node influence field is constructed according to the influence range and the attenuation rate, and multiple node influence fields are superimposed to generate a distribution influence map; The real-time change characteristics of the casualty movement trajectory map are converted into dynamic weights, and the distribution influence map is dynamically weighted to obtain a density distribution model; Divide the target area into grid cells, calculate the distance between the center of each grid cell and the key nodes, calculate the reference density value within each grid cell according to the distance and the density distribution model, and superimpose the time-varying characteristics of the dynamic weight on the reference density value to obtain the casualty distribution density reflecting the dynamic distribution of personnel.

[0009] In an alternative embodiment, Based on the scenario situation assessment result, divide the rescue area according to the casualty distribution density, and determine the resource allocation weight of each rescue area based on the regional danger level. The generated medical resource scheduling plan includes: Receive the scenario situation assessment result and the casualty distribution density data, construct a prediction model based on the cellular automaton, analyze the change trend of the scenario situation, and obtain the scenario threat degree and the passable path of each area at future moments; Calculate the density difference between adjacent areas according to the casualty distribution density data, mark the areas where the density difference is less than the density threshold and the spatial distance is less than the distance threshold as the areas to be merged, and optimize the boundaries of the areas to be merged in combination with the scenario threat degree to generate rescue partitions; Based on the corresponding relationship between the medical resource input amount and the casualty treatment effect in the historical rescue data, calculate the resource utilization efficiency of each rescue partition, and calculate the regional danger level according to the casualty distribution density, scenario threat degree and resource utilization efficiency within the rescue partition; Construct a resource transfer network with the rescue partitions as nodes and the passable paths as edges, perform weighted calculation on the regional danger level and the resource utilization efficiency to obtain the resource allocation weight, and generate a medical resource scheduling plan with the minimum transfer loss based on the resource transfer network; Monitor the change of the scenario situation assessment result. When the change of the scenario threat degree exceeds the situation threshold, re-divide the rescue partition based on the updated scenario threat degree and the casualty distribution density, adjust the regional danger level and the resource allocation weight, and update the medical resource scheduling plan.

[0010] In an alternative embodiment, Construct a prediction model based on the cellular automaton, and analyze the change trend of the scenario situation. Obtaining the scenario threat degree and the passable path of each area at future moments includes: Divide the rescue scenario into regular grid cells, map the road traffic status, building damage degree, hazard source intensity and environmental threat degree in the scenario situation data to each grid cell to form an initial state matrix of the grid cells; Calculate the evolution value of the road traffic status according to the passing frequency of the rescue vehicle, calculate the evolution value of the building damage degree according to the secondary disaster monitoring data, calculate the diffusion value of the hazard source intensity and the environmental threat degree between adjacent grid cells, and construct a state transition equation with the evolution value of the road traffic status, the evolution value of the building damage degree, the hazard source intensity and the diffusion value of the environmental threat degree; The state transition equation is solved by iterative calculation to obtain the state matrix of each grid cell at the prediction moment, and the scene threat degree at the prediction moment is calculated according to the state matrix; The passing probability is calculated based on the road passing state and the scene threat degree of the grid cell, a path cost function is constructed, and a passable path is obtained by using a search algorithm; The scene situation data is received in real time. When the data is updated, the updated data is mapped to a new initial state matrix, and the prediction calculation is re-executed to dynamically update the scene threat degree and the passable path.

[0011] In an optional embodiment, A three-dimensional model of the emergency site is constructed in the digital twin platform, the scene threat degree in the scene situation assessment result and the medical resource scheduling scheme are visually mapped, rescue situation annotations are superimposed in real time through augmented reality technology, and the position information of rescue personnel and medical equipment is updated in real time based on the target tracking algorithm, including: In the digital twin platform, on-vehicle lidar is used to obtain the point cloud data of the emergency site, the point cloud data is registered to obtain a dense point cloud, the scene structure of the dense point cloud is segmented based on the region growing algorithm to generate a three-dimensional model of the emergency site, and a unified spatial coordinate system is established in the three-dimensional model; Based on the unified spatial coordinate system, the scene threat degree in the scene situation assessment result is mapped to the three-dimensional model to form a situation heat map, the equipment layout data and personnel deployment data in the medical resource scheduling scheme are converted into position coordinates in the three-dimensional scene and situation annotations are generated, and a digital twin scene with situation information is obtained; The feature point matching algorithm is used to realize the real-time registration of the mobile device camera and the digital twin scene with situation information, establish the mapping relationship between the camera view and the unified spatial coordinate system, and superimpose and display the situation heat map and situation annotations in the camera view through augmented reality technology to obtain an augmented reality situation view; The position information of the medical equipment is obtained in real time based on the target tracking algorithm, the position information collected by the rescue personnel positioning terminal is received, the position information is coordinate-transformed according to the unified spatial coordinate system, and the transformed position information is superimposed and displayed in the augmented reality situation view based on the mapping relationship; When the scene situation assessment result is updated, a new situation heat map is regenerated; when the medical resource scheduling scheme changes, the situation annotations are updated; when the position information of the rescue personnel and medical equipment is updated, the position display in the augmented reality situation view is updated in real time based on the mapping relationship.

[0012] In the second aspect of the embodiments of the present invention, A sudden event emergency medical command system based on AI and the Internet of Things is provided, including: The first unit is used to collect thermal imaging data of the emergency scene through an infrared sensor, extract casualty body temperature distribution data and casualty location data from the thermal imaging data, determine the environmental monitoring range according to the casualty location data, and collect environmental parameter data through an environmental sensor within the range; The second unit is used to construct a body temperature change trend graph based on the casualty body temperature distribution data, calculate the body temperature abnormality degree value, calculate the environmental threat degree value according to the environmental parameter data at the same time, calculate the regional danger level based on the body temperature abnormality degree value and the environmental threat degree value, count the casualty distribution density in combination with the casualty location data, and generate a scene situation assessment result according to the regional danger level and the casualty distribution density; The third unit is used to divide the rescue area according to the casualty distribution density based on the scene situation assessment result, determine the resource allocation weight of each rescue area based on the regional danger level, and generate a medical resource scheduling plan; The fourth unit is used to construct a three-dimensional model of the emergency scene in the digital twin platform, perform visual mapping on the scene situation assessment result and the medical resource scheduling plan, overlay rescue situation annotations in real time through augmented reality technology, and update the location information of rescue personnel and medical equipment in real time based on the target tracking algorithm; The fifth unit is used to dynamically adjust the medical resource scheduling plan through a recursive optimization algorithm according to the location information of rescue personnel and medical equipment, and realize the real-time optimal allocation of medical resources.

[0013] In the third aspect of the embodiments of the present invention, There is provided an electronic device, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0014] In the fourth aspect of the embodiments of the present invention, There is provided a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0015] In this embodiment, thermal imaging data and environmental parameter data of the emergency scene are collected through infrared sensors and environmental sensors. By combining the body temperature distribution and location information of the wounded, a comprehensive situation awareness of the emergency scene is achieved. This method can quickly and accurately evaluate the on-site situation, providing reliable data support for emergency decision-making. Based on the results of the scenario situation assessment, an intelligent algorithm is used to divide the rescue area and allocate resources, generating a scientific and reasonable medical resource scheduling plan. This method can effectively improve the utilization efficiency of medical resources and ensure the orderly progress of rescue work. By using digital twin and augmented reality technologies, three-dimensional visualization and real-time situation update of the emergency scene are realized. At the same time, the medical resource scheduling plan is dynamically adjusted through a recursive optimization algorithm to achieve real-time optimal allocation of medical resources. This method greatly improves the scientificity and efficiency of emergency command, contributing to enhancing the emergency rescue ability for emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of the method for emergency medical command for emergencies based on AI and the Internet of Things according to an embodiment of the present invention; Figure 2 is a comparative analysis chart of key indicators according to an embodiment of the present invention; Figure 3 is a simulation analysis chart of the path safety factor changing with time according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of 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.

[0018] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0019] Figure 1 is a flowchart of the method for emergency medical command for emergencies based on AI and the Internet of Things according to an embodiment of the present invention, as Figure 1 shown, the method includes: Collect thermal imaging data of the emergency scene through an infrared sensor, extract the body temperature distribution data and the location data of the wounded from the thermal imaging data, determine the environmental monitoring range according to the location data of the wounded, and collect environmental parameter data through an environmental sensor within the range; Construct a temperature change trend graph based on the body temperature distribution data of the wounded, calculate the value of the degree of abnormal body temperature, and at the same time calculate the value of the degree of environmental threat based on the environmental parameter data. Calculate the regional danger level based on the value of the degree of abnormal body temperature and the value of the degree of environmental threat, and combine the wounded location data to count the distribution density of the wounded. Generate a scene situation assessment result based on the regional danger level and the wounded distribution density; Based on the scene situation assessment result, divide the rescue area according to the wounded distribution density, and determine the resource allocation weight of each rescue area based on the regional danger level to generate a medical resource scheduling plan; Construct a three-dimensional model of the emergency scene in the digital twin platform, perform visual mapping on the scene situation assessment result and the medical resource scheduling plan, overlay the rescue situation annotation in real time through augmented reality technology, and update the location information of rescue personnel and medical equipment in real time based on the target tracking algorithm; According to the location information of rescue personnel and medical equipment, dynamically adjust the medical resource scheduling plan through a recursive optimization algorithm to achieve real-time optimal allocation of medical resources.

[0020] For example, set an environmental monitoring range with a radius of 50 meters centered on the location of the wounded, and deploy an environmental sensor network within this range. Environmental sensors include temperature sensors, humidity sensors, toxic gas sensors, and dust concentration sensors, etc., with a sampling frequency of 1Hz. The measurement range of the temperature sensor is -40°C to 85°C, and the accuracy is ±0.5°C; the measurement range of the humidity sensor is 0-100%RH, and the accuracy is ±3%RH; the toxic gas sensor detects the concentration of gases such as CO and SO2; the dust sensor detects the concentration of PM2.5 and PM10.

[0021] In an alternative embodiment, Collect the thermal imaging data of the emergency scene through an infrared sensor. The body temperature distribution data and the wounded location data extracted from the thermal imaging data include: Collect the thermal imaging data of the emergency scene through an infrared sensor, and perform adaptive histogram equalization enhancement processing on the thermal imaging data to obtain enhanced thermal imaging data. Determine the filter window size based on the local temperature variance of the enhanced thermal imaging data, and use the adaptive median filter algorithm to denoise the enhanced thermal imaging data to obtain denoised thermal imaging data; Establish a mapping function between the gray value and the actual temperature according to the temperature response characteristics of the infrared sensor, convert the gray value in the denoised thermal imaging data into a temperature value to generate a temperature distribution map, set a double-threshold detection interval based on the normal body temperature range of the human body, and perform regional connectivity analysis on the pixel points whose temperature values in the temperature distribution map fall within the double-threshold detection interval to obtain the candidate wounded area; Perform morphological processing on the candidate casualty area, extract the contour features, area features, and aspect ratio features of the area, and screen out the areas that do not conform to the features according to the human body size feature template to obtain the confirmed casualty area; Establish a temperature field distribution model for the confirmed casualty area, and extract the temperature numerical sequence, temperature gradient distribution, and temperature uniformity features within the area as the casualty body temperature distribution data; calculate the minimum bounding rectangle according to the contour features of the confirmed casualty area, and use the coordinates of the center point of the rectangle as the casualty position data.

[0022] Exemplarily, deploy an infrared sensor array at the scene of the emergency for thermal imaging acquisition. The infrared sensor can use an uncooled vanadium oxide detector, with a wavelength response range of 8 - 14 microns, a temperature resolution of 0.05 °C, and an image resolution of 640 × 480 pixels. The sensor array consists of 9 sensor units, arranged in a 3×3 matrix, with an adjacent sensor field of view overlap rate of 20%, enabling a large-range scan of 120 degrees × 90 degrees. The acquisition frequency is set to 30 Hz, and each frame of thermal imaging data contains the scene temperature grayscale information.

[0023] First, perform histogram equalization enhancement processing on the acquired thermal imaging data. Adaptively divide the grayscale interval according to the image grayscale distribution characteristics, and focus on enhancing the low-contrast areas. In practical applications, divide the grayscale range of the thermal imaging data into 256 levels, calculate the pixel cumulative distribution of each grayscale level, generate the cumulative distribution function, and map the original grayscale values to a new grayscale space through this function to achieve adaptive enhancement of the image contrast.

[0024] Perform adaptive median filtering for noise reduction after image enhancement. First, calculate the local temperature variance of the thermal imaging data. When the local area temperature variance is greater than 2 °C, it indicates that the area contains more detailed information, and a 3×3 small window is used for filtering; when the local temperature variance is less than 0.5 °C, it indicates that the area is relatively flat, and a 7×7 large window is used for filtering. During the filtering process, retain the median pixel within the window to effectively remove the salt-and-pepper noise and random noise in the thermal imaging data.

[0025] Establish a conversion relationship between the grayscale value and the actual temperature based on the temperature response characteristics of the infrared sensor. By collecting the thermal imaging data of a standard blackbody at different ambient temperatures, obtain the point set of the corresponding relationship between the grayscale value and the temperature. Use the piecewise linear interpolation method to establish the mapping function to convert the grayscale value in the noise-reduced thermal imaging data into specific temperature values. For example, the grayscale value 0 corresponds to the ambient temperature of 20 °C, the grayscale value 255 corresponds to the highest temperature of 50 °C, and the intermediate temperature values are obtained through linear interpolation.

[0026] Set a double-threshold detection interval for casualty identification. Considering the normal body temperature range of humans and measurement errors, set the lower detection limit to 35°C and the upper limit to 40°C. Traverse the temperature distribution map and mark all pixel points that fall within this temperature range. Use the 8-neighborhood connectivity analysis method to perform region growing on the marked pixel points. When the temperature difference between adjacent pixel points is less than 0.5°C, they are determined to be in the same region, and finally, multiple candidate casualty regions are obtained.

[0027] Perform morphological processing on the candidate casualty regions, including opening operation to remove small-area noise regions and closing operation to fill holes within the regions. Extract the contour features of the regions, calculate the perimeter and area of the contours; extract the aspect ratio features, and calculate the aspect ratio of the minimum bounding rectangle of the region. Set the screening conditions according to the human body size feature template: the area of the region should be within the range of 0.3 - 2 square meters, and the aspect ratio should be between 1.5 - 4. Regions that do not meet the conditions are excluded to obtain the confirmed casualty regions.

[0028] For each confirmed casualty region, establish a temperature field distribution model. First, extract the temperature numerical sequence within the region, including statistical features such as the highest temperature, lowest temperature, and average temperature. Then calculate the temperature gradient distribution. The Sobel operator can be used to calculate the temperature gradients in the x and y directions, and the gradient magnitude and direction are synthesized. Finally, calculate the temperature uniformity feature, which can be represented by the standard deviation or entropy value of the temperature within the region. These features together constitute the casualty body temperature distribution data.

[0029] Finally, calculate the minimum bounding rectangle according to the contour features of the confirmed casualty region. The calculation of the minimum bounding rectangle can be achieved by using the rotational Kalman filter algorithm. Take the image coordinates of the center point of the rectangle as the casualty position data. If necessary, the image coordinates can also be converted into actual geographical coordinates for subsequent rescue positioning.

[0030] In this embodiment, it can effectively process the thermal imaging data at the scene of emergencies, accurately extract the body temperature distribution and position information of casualties, and provide an important basis for emergency rescue. This method adopts an image preprocessing technology that combines adaptive histogram equalization and adaptive median filtering, effectively improving the quality and reliability of thermal imaging data. By establishing an accurate mapping relationship between the gray value and the actual temperature, combined with the double-threshold detection method of human body temperature characteristics, it can accurately identify potential casualty regions. The method that combines morphological processing and the human body size feature template effectively removes misdetected regions and improves the accuracy of casualty identification. It can not only obtain the position information of casualties but also extract detailed body temperature distribution features, including the temperature numerical sequence, temperature gradient distribution, and temperature uniformity, etc. These information are of great value for evaluating the condition of casualties and judging the severity of injuries, and can provide more comprehensive decision-making support for rescue personnel.

[0031] In an alternative embodiment, Construct a temperature change trend graph based on the body temperature distribution data of the wounded, calculate the value of the degree of body temperature abnormality, and at the same time calculate the value of the environmental threat degree according to the environmental parameter data. Calculate the regional danger level based on the value of the body temperature abnormality degree and the environmental threat degree, and combine the wounded location data to count the wounded distribution density. Generate the scenario situation assessment result according to the regional danger level and the wounded distribution density, including: Establish a body temperature monitoring window, divide the body temperature monitoring window into an observation interval and a prediction interval, perform piecewise fitting on the body temperature data in the observation interval to extract the fluctuation law, establish a body temperature baseline based on the prediction interval, calculate the degree of deviation of the fluctuation law from the body temperature baseline to obtain the fluctuation abnormality feature, and judge the value of the body temperature abnormality degree according to the fluctuation abnormality feature; Construct an influence network containing environmental parameters according to the environmental parameter data, use the leading abnormal parameter as the root node and the affected parameter as the child node, establish a directed graph structure of the transfer relationship between parameters, analyze the abnormal diffusion path based on the directed graph structure, and calculate the environmental threat degree value in combination with the range and speed of abnormal diffusion; Dynamically update the inference rules of the neural network according to the change trends of the body temperature abnormality degree value and the environmental threat degree value, and use the inference rules to calculate the regional danger level; Conduct a spatial distribution analysis on the wounded location data, establish a wounded movement trajectory graph, extract the trajectory intersection points as key nodes, and calculate the degree of personnel aggregation at each key node to obtain the wounded distribution density; Use the regional danger level as the weight coefficient to perform weighted calculation on the wounded distribution density to obtain the scenario situation assessment result.

[0032] Exemplarily, by monitoring the body temperature of the wounded in real time, a body temperature monitoring window is established. The time range of the body temperature monitoring window is divided into two parts. One part is the observation interval, which is used to record the body temperature data in the past period of time, and the other part is the prediction interval, which is used to predict the body temperature change trend in the future period of time. The body temperature data in the observation interval will be divided into several time periods, and the body temperature data in each time period will be curve-fitted to extract the change law of body temperature fluctuation. This fitting can adopt multi-segment linear fitting or smooth curve fitting methods, and the purpose is to restore the basic trend and fluctuation amplitude of body temperature change over time. Then, in the prediction interval, based on the body temperature change law obtained in the observation interval, a body temperature baseline is established as a reference value for normal body temperature. Compare the actual body temperature data in the prediction interval with the baseline to judge whether the body temperature fluctuation deviates from the normal range. The greater the degree of deviation of the body temperature fluctuation, the higher the possibility of abnormal body temperature of the wounded. By setting different deviation thresholds, a body temperature abnormality degree value is finally obtained to quantify the severity of body temperature abnormality.

[0033] Next, collect environmental parameter data. Environmental parameters include, but are not limited to, temperature, humidity, air pressure, harmful gas concentration, light intensity, etc. Among these environmental parameters, first determine which parameters belong to leading abnormal parameters, such as sudden increase in harmful gas concentration, abnormal temperature change, etc. These parameters will serve as the root nodes in the network. Then, based on the mutual influence relationship between environmental parameters, establish a directed graph structure that includes all relevant parameters. This directed graph reflects the transmission path between parameters. For example, an increase in temperature will cause a decrease in humidity or air pressure fluctuations. According to the influence intensity and transmission order between parameters, analyze how anomalies spread between environmental parameters, and identify the path, speed, and scope of anomaly spread. Considering these factors comprehensively, calculate the environmental threat degree value to measure the threat degree of the current environmental anomaly to regional security.

[0034] Use the body temperature anomaly degree value and the environmental threat degree value as inputs to dynamically update the rule base of an inference system based on a neural network. This neural network has been trained to infer the corresponding regional danger level according to different input values. During the inference process, the neural network will consider the change trends of body temperature anomaly and environmental threat, perform weighted processing on the input data, and output the corresponding regional danger level. The regional danger level is divided into multiple levels, from low risk to high risk, and each level represents the current comprehensive danger degree of the region.

[0035] Conduct a spatial distribution analysis on the location data of the wounded. Specifically, collect the current and past location coordinates of the wounded, and use this data to draw a movement trajectory map of the wounded. By analyzing the intersection points between the trajectories of each wounded person, extract the key location nodes where the wounded may gather. Calculate the degree of personnel aggregation for each key node. Specifically, the distribution density of the wounded can be obtained by counting the number of wounded people who have appeared at this location and their stay time.

[0036] Use the obtained regional danger level as the weight coefficient and perform weighted calculation with the wounded distribution density. The higher the regional danger level, the greater the risk of the wounded gathering in this region. Through this weighted method, generate the situation assessment result of the current scene. The situation assessment result can comprehensively reflect the body temperature anomaly, environmental threat, wounded distribution, and comprehensive danger degree of the current scene, providing a basis for rescue decision-making and resource allocation.

[0037] In this embodiment, by establishing a body temperature monitoring window and performing segmented fitting analysis, the changing trend of body temperature can be accurately captured, potential abnormal situations can be detected in advance, and the precise assessment and early warning of the health status of the wounded can be realized. Based on the analysis method of the environmental parameter influence network, the correlation and conduction mechanism between environmental factors can be effectively identified, the potential threat of environmental changes to the wounded can be accurately evaluated, and a more comprehensive basis for environmental risk prevention and control can be provided. By using a dynamically updated deep neural network combined with the analysis of the distribution density of the wounded, the precise assessment of the overall situation of the area is realized, which can provide a scientific basis for the allocation of medical resources and emergency response, and improve the efficiency of emergency rescue.

[0038] In an alternative embodiment, Perform a spatial distribution analysis on the location data of the wounded, establish a moving trajectory map of the wounded, extract the trajectory intersection points as key nodes, and calculate the degree of personnel aggregation at each key node to obtain the distribution density of the wounded, including: Perform segmented processing on the location data of the wounded according to the time sequence to obtain a time window sequence, calculate the change of the position coordinates within the time window sequence to obtain a displacement vector, connect the displacement vectors in sequence to construct a moving trajectory map of the wounded, perform smoothing processing on the moving trajectory map of the wounded to eliminate abnormal jumps, and extract the target moving path according to the smoothed trajectory; Draw the target moving path in the spatial coordinate system, calculate the intersection angle and the shortest distance between the paths, determine the trajectory intersection points when the intersection angle and the shortest distance meet the preset conditions, count the intersection frequencies at each trajectory intersection point, calculate the number of personnel within the unit area around the trajectory intersection points to obtain the degree of personnel aggregation, and determine the key nodes according to the weighted values of the intersection frequencies and the degree of personnel aggregation; Establish a local coordinate system with the key nodes as the center, map the intersection frequencies and the degree of personnel aggregation to the size of the influence range and the attenuation rate respectively, construct a node influence field according to the influence range and the attenuation rate, and superimpose multiple node influence fields to generate a distribution influence map; Convert the real-time change characteristics of the moving trajectory map of the wounded into dynamic weights, and perform dynamic weighting on the distribution influence map to obtain a density distribution model; Divide the target area into grid cells, calculate the distance between the center of each grid cell and the key nodes, calculate the reference density value within each grid cell according to the distance and the density distribution model, and superimpose the time-varying characteristics of the dynamic weights on the reference density value to obtain the distribution density of the wounded reflecting the dynamic distribution of personnel.

[0039] Exemplarily, the real-time position data of all the wounded are collected, and these position data are segmented according to the time sequence and divided into a sequence of continuous time windows. Within each time window, the position information of the wounded in this time period is extracted, and the change of the position coordinates between adjacent time points is calculated to obtain the corresponding displacement vector. These displacement vectors are sequentially connected in time sequence to form a complete movement trajectory graph of each wounded throughout the time period. To avoid abnormal jumps caused by data noise or positioning errors in a short period, trajectory smoothing processing techniques are adopted, such as using methods like moving average or Kalman filtering to correct the mutation points in the trajectory, thereby ensuring the continuity and smoothness of the trajectory curve, and then extracting the target movement path representing the real movement trend.

[0040] The target movement paths of all the wounded are plotted in a unified spatial coordinate system. All the paths are compared pairwise, and the crossing angle between each pair of paths and the shortest distance between them are calculated. When the crossing angle between a certain pair of paths is within a small range and the shortest distance between the paths is lower than the set threshold condition, it is determined that there is a crossing point in space for these two paths, which is marked as a trajectory crossing point. The frequency of all the trajectory crossing points is counted, that is, the number of times each crossing point is passed by the paths of different wounded. At the same time, a unit area is demarcated around each trajectory crossing point, and the number of people present in this area is counted to obtain the degree of personnel aggregation around the crossing point. Then, the crossing frequency and the degree of personnel aggregation of each crossing point are weighted and calculated according to the preset weight coefficient, and the positions with high personnel aggregation and high path crossing are screened out and determined as key nodes.

[0041] Suppose a crossing point is found at the coordinate (50, 50), and 5 trajectories pass through this point, with a crossing frequency of 5. 20 people are counted within a radius of 10 meters from this point, then the degree of personnel aggregation is 0.064 people per square meter. If the weight of the crossing frequency is set to 0.6 and the weight of the degree of personnel aggregation is set to 0.4, then the weighted value of this point is 5×0.6 + 0.064×0.4 = 3.0256. A threshold can be set, such as 2.5. When the weighted value exceeds this threshold, this crossing point is determined as a key node.

[0042] At the position of each key node, a local coordinate system centered on this node is established. The previously calculated crossing frequency is mapped to the size of the influence range, that is, the higher the crossing frequency, the larger the influence range; at the same time, the degree of personnel aggregation is mapped to the attenuation rate, that is, the higher the aggregation degree, the slower the influence decays within the influence range. Based on the influence range and the attenuation rate, the influence field of each key node is constructed, which is manifested as a spatial influence model that spreads outward from the center and gradually weakens with the increase of distance. The influence fields of all the key nodes are superimposed to obtain an overall distribution influence map, which reflects the comprehensive intensity of the influence of personnel aggregation and trajectory crossing at each position in the entire region.

[0043] Extract the characteristics of the change of the movement trajectory map of the wounded over time, calculate parameters such as the speed of trajectory change and the frequency of direction change, and convert these change characteristics into dynamic weight values. Using these dynamic weights, perform dynamic weighting on the previously generated distribution influence map, so that the distribution influence map not only reflects the static aggregation degree, but also reflects the time-varying characteristics of the personnel distribution, and forms a density distribution model.

[0044] Divide the target area into several grid cells, and each grid cell has a center point. Calculate the distance from the center point of each grid cell to all key nodes, and calculate the reference density value within each grid cell according to the distance and the density distribution model. The closer the unit is to the key node, the higher its reference density value. Finally, superimpose the time change characteristics of the dynamic weight on the reference density value to obtain the wounded distribution density result that can reflect the dynamic distribution change of personnel in real time.

[0045] Figure 2 This is a comparative analysis chart of the key indicators of the embodiment of the present invention. As Figure 2 shown, from the perspective of positioning accuracy, this method achieves a high accuracy of 1.25 meters, an improvement of 66.8% compared with the traditional density heat map, which is mainly due to the algorithm optimization of trajectory smoothing to eliminate abnormal jumps. In terms of the accuracy of personnel density prediction, this method achieves an accuracy of 92.7%, far ahead of the 78.3% of the traditional method, verifying the effectiveness of the key node determination mechanism based on cross-frequency and personnel aggregation degree weighting. The dynamic change response time is only 1.8 seconds, more than 3 times faster than the traditional technology, reflecting the advantage of the proposed density distribution model in capturing time-varying characteristics. The key node recognition rate and the abnormal trajectory detection rate reach 94.2% and 89.5% respectively, both far superior to the existing technology, indicating that this method can better identify trajectory intersections and screen key nodes. The calculation efficiency index is 4.2 ms / sample, more than twice as fast as the GPS-based trajectory analysis, demonstrating the high efficiency after algorithm optimization. These data fully prove the significant advantages of the proposed wounded trajectory analysis and density modeling method in various key indicators.

[0046] The prior art usually analyzes the distribution of personnel only through static location data, ignoring the dynamic changes and interaction characteristics of the movement trajectories of personnel, resulting in inaccurate assessment of personnel aggregation situations. Especially in complex disaster environments, high-risk aggregation areas cannot be effectively identified. In this application, a time window is introduced to segment the location data, a continuous movement trajectory map is constructed, and smoothing processing is used to eliminate noise and extract the real movement path. At the same time, by calculating the path intersection points, their intersection frequencies, and the degree of personnel aggregation, key nodes are identified, and by establishing a local influence field, an accurate description of the dynamic personnel aggregation situation in the space is achieved. In addition, a dynamic weight mechanism is introduced to map the trajectory change characteristics to the density distribution model to further reflect the characteristics of personnel flow. The starting point of the improvement is to solve the limitations of static and single analysis in the prior art and achieve dynamic and spatio-temporal combined distribution density assessment. Ultimately, the recognition accuracy of crowded areas is improved, providing more reliable data support for subsequent hazard warning and rescue decision-making.

[0047] In an alternative embodiment, Based on the results of the scenario situation assessment, the rescue area is divided according to the distribution density of the wounded, and the resource allocation weight of each rescue area is determined based on the regional hazard level. The generated medical resource scheduling plan includes: Receiving the results of the scenario situation assessment and the distribution density data of the wounded, constructing a prediction model based on the cellular automaton, analyzing the change trend of the scenario situation, and obtaining the scenario threat degree and passable paths of each area at future moments; Calculating the density difference between adjacent areas according to the distribution density data of the wounded, marking the areas where the density difference is less than the density threshold and the spatial distance is less than the distance threshold as areas to be merged, and optimizing the boundaries of the areas to be merged in combination with the scenario threat degree to generate rescue sub-areas; Based on the corresponding relationship between the medical resource input volume and the treatment effect of the wounded in the historical rescue data, calculating the resource utilization efficiency of each rescue sub-area, and calculating the regional hazard level according to the distribution density, scenario threat degree, and resource utilization efficiency of the wounded in the rescue sub-area; Constructing a resource transfer network with the rescue sub-areas as nodes and the passable paths as edges, performing weighted calculations on the regional hazard level and resource utilization efficiency to obtain the resource allocation weight, and generating a medical resource scheduling plan with the minimum transfer loss based on the resource transfer network; Monitoring the change of the results of the scenario situation assessment. When the change of the scenario threat degree exceeds the situation threshold, re-dividing the rescue sub-areas based on the updated scenario threat degree and the distribution density of the wounded, adjusting the regional hazard level and resource allocation weight, and updating the medical resource scheduling plan.

[0048] Exemplarily, receive the results of the scene situation assessment and the casualty distribution density data, and divide the entire rescue area into several grid cells. Each grid cell represents a small geographical area and contains corresponding casualty density and danger level information.

[0049] Use the cellular automaton model to simulate these grid cells. The cellular automaton is a discrete model that simulates the dynamic evolution of complex systems by defining simple local rules. Set the initial state for each grid cell, including the current danger level and personnel density. According to the influence rules of neighboring cells, iteratively update the state of each cell to predict the change trend of the scene threat degree and the passable paths in each area at future moments.

[0050] Suppose a rescue area is divided into a 10x10 grid, and the initial threat degree of each grid cell is between 0 and 10. Set the update rule as: if the average threat degree of the 8 adjacent cells around is greater than the current cell, the threat degree of the current cell increases by 1; otherwise, it decreases by 1. After 100 iterations, the future threat degree distribution and passable paths of this area can be obtained.

[0051] After obtaining the personnel density and predicted scene threat degree of each cell, compare the casualty density data of adjacent grid cells and calculate the density difference between them. If the density difference between two cells is lower than the preset density threshold and their spatial distance is less than the distance threshold, then mark these two cells as areas to be merged. For all areas to be merged that meet the conditions, based on considering the predicted scene threat degree, merge the areas with relatively low local threat and similar density by adjusting the boundaries to obtain the final rescue zones.

[0052] Then, based on the corresponding relationship between the amount of medical resources invested and the casualty treatment effect in historical rescue data, establish a resource efficiency model to evaluate the actual treatment efficiency after investing medical resources in each rescue zone. Comprehensively consider the current casualty distribution density, predicted scene threat degree, and resource utilization efficiency of each rescue zone, and calculate the regional danger level of this zone. The weighted average method can be used. Suppose the weights are 0.4, 0.4, and 0.2 respectively. For example, if the casualty density in a zone is 80 people per square kilometer (normalized to 8 points), the threat degree is 7 points, and the resource utilization efficiency is 6 points, then its danger level is 7.2 points.

[0053] Taking the generated rescue zones as nodes and the predicted passable paths as edges, a resource transfer network graph is constructed to reflect the feasible paths and distance relationships of resource scheduling among different zones. On this basis, the regional hazard levels and resource utilization efficiencies of each zone are weighted to determine the resource allocation weights for each zone, ensuring that resources are inclined towards high-hazard and high-demand areas. Using the shortest path algorithm, combined with the resource allocation weights and path loss situations, a medical resource scheduling plan with the minimum resource transfer loss is generated to ensure the efficient transfer of medical resources while meeting the demands.

[0054] Real-time monitor the results of the scenario situation assessment. When it is detected that the scenario threat level in a certain area has changed significantly and exceeds the preset situation change threshold, based on the updated casualty distribution density and threat level data, re-divide the rescue zones, re-adjust the hazard levels and resource allocation weights of each zone, and dynamically correct the medical resource scheduling plan to ensure the real-time and rationality of resource allocation.

[0055] In this embodiment, by introducing a cellular automaton prediction model, the threat trends and passable paths of each area in the future can be accurately analyzed, improving the forward-looking of rescue operations. Dynamically optimize the area division based on the casualty distribution density and scenario threat level to ensure reasonable adjustment of the rescue area boundary, reduce duplicate resource investment and scheduling blind spots. Combining historical rescue data, real-time evaluate the resource utilization efficiency and hazard level of each area, rationally allocate limited medical resources, and improve the accuracy and utilization rate of resource allocation. After constructing the resource transfer network, by dynamically adjusting the resource allocation weights, effectively reduce the loss in the process of resource scheduling, and realize the rapid and efficient transfer of resources. The overall technical means can respond to on-site threat changes in real time, ensure that medical resources always accurately and efficiently cover key areas, and significantly improve the overall rescue efficiency.

[0056] In an alternative embodiment, Based on the cellular automaton, a prediction model is constructed to analyze the change trend of the scenario situation. The scenario threat levels and passable paths of each area at future moments obtained include: Divide the rescue scenario into regular grid cells, map the road traffic status, building damage degree, hazard source intensity, and environmental threat level in the scenario situation data to each grid cell to form the initial state matrix of the grid cells; Calculate the evolution value of the road traffic status according to the passing frequency of rescue vehicles, calculate the evolution value of the building damage degree according to the secondary disaster monitoring data, calculate the diffusion value of the hazard source intensity and environmental threat level between adjacent grid cells, and construct the state transition equation with the road traffic status evolution value, building damage degree evolution value, hazard source intensity, and environmental threat level diffusion value; Use iterative calculation to solve the state transition equation to obtain the state matrix of each grid cell at the prediction moment, and calculate the scenario threat level at the prediction moment according to the state matrix. Calculate the passing probability based on the road traffic status and scene threat level of grid cells, construct a path cost function, and use a search algorithm to solve for the passable path; Receive scene situation data in real time. When the data is updated, map the updated data to a new initial state matrix, re - execute the prediction calculation, and dynamically update the scene threat level and the passable path.

[0057] Exemplarily, divide the rescue scene into m×n regular grid cells. The size of each grid cell can be set according to actual needs, such as 10m×10m. For each grid cell, record four attribute values: its road traffic status, building damage degree, hazard source intensity, and environmental threat level. The road traffic status is represented by a value between 0 and 1, where 0 means completely blocked and 1 means completely unblocked. The building damage degree is represented by 0 - 100%, where 0% means intact and 100% means completely damaged. Both the hazard source intensity and the environmental threat level are represented by values between 0 and 10, where 0 means no threat and 10 means the highest threat level.

[0058] Next, map the scene situation data into the grid cells. For example, if the traffic status of a certain road is 0.8, then set the road traffic status of the grid cell where it is located to 0.8. If the damage degree of a certain building is 60%, then set the building damage degree of the grid cell where it is located to 60%. If the hazard source intensity at a certain place is 6, then set the hazard source intensity of the grid cell where it is located to 6. The environmental threat level data is mapped in the same way. In this way, an initial state matrix of the grid cells is formed.

[0059] Then, construct a state transition equation. First, calculate the evolution value of the road traffic status. Count the vehicle passing frequency of each grid cell within a certain time. The higher the frequency, the smoother the road. For example, if 100 vehicles pass through a certain grid cell in 1 hour, then its road traffic status evolution value can be set to 0.9. For the building damage degree, calculate its evolution value based on the monitored secondary disaster data (such as aftershock intensity). For example, if the aftershock intensity is monitored to be 4, then the building damage degree evolution value can be set to 10%. For the hazard source intensity and the environmental threat level, calculate their diffusion values to adjacent grid cells. The diffusion value is inversely proportional to the distance and directly proportional to the initial intensity. For example, if the hazard source intensity of a certain grid cell is 8, then the hazard source intensity diffusion value of its adjacent grid cell can be set to 4.

[0060] Using the above evolution values and diffusion values, construct a state transition equation. For each grid cell, its state at time t + 1 is equal to its state at time t plus the evolution value and the diffusion value. For example, if the road traffic status of a certain grid cell at time t is 0.7 and the evolution value is 0.1, then the road traffic status at time t + 1 is 0.8. The calculation methods for the building damage degree, hazard source intensity, and environmental threat level are similar.

[0061] An iterative calculation method is used to solve the state transition equation. Set the prediction time length, such as predicting the situation in the next 2 hours. Discretize the time. For example, if each time step is 5 minutes, then 24 iterations are required. The state of all grid cells is updated once in each iteration. After the iteration is completed, the state matrix at the prediction moment is obtained.

[0062] Based on the predicted state matrix, calculate the scene threat level. The scene threat level is comprehensively determined by three factors: the degree of building damage, the intensity of the hazard source, and the environmental threat level. The weighted average method can be used. For example, the scene threat level = 0.3×the degree of building damage + 0.4×the intensity of the hazard source + 0.3×the environmental threat level. Calculate the threat level for each grid cell and draw a heat map to visually display the threat levels of each area.

[0063] Next, calculate the passable paths. First, calculate the passing probability based on the road passing state and the scene threat level of the grid cells. The passing probability is directly proportional to the road passing state and inversely proportional to the scene threat level. For example, if the road passing state of a certain grid cell is 0.8 and the scene threat level is 3, then its passing probability can be set to 0.8×(1 - 3 / 10) = 0.56. Then construct a path cost function. The cost is inversely proportional to the passing probability and directly proportional to the path length. Use search algorithms such as A* to solve for the passable paths with the goal of minimizing the total cost.

[0064] Finally, receive the updated scene situation data in real time. When new data is received, map it to a new initial state matrix and re - execute the prediction calculation process. This can dynamically update the scene threat level and the passable paths to ensure the timeliness of decision - making.

[0065] Figure 3 This is the simulation analysis diagram of the path safety factor changing with time in the embodiment of the present invention, as Figure 3 shown. This figure shows a comparative analysis of the rescue path planning technology based on cellular automata and traditional methods in terms of the change of the safety factor with time. Through Monte Carlo simulation (10,000 iterations), the figure intuitively presents the changing trends of the safety factor time series of three methods: the technical method of the present invention (circular markers, solid line), static path planning (triangle markers, dashed line), and the GIS - based method (square markers, dotted line).

[0066] The results show that during the dynamic change process of the disaster environment, this technical method always maintains a relatively high safety factor (with an average of 0.85). Even at the critical moments of secondary disasters occurring at 30 minutes and the environmental threat increasing at 60 minutes, its safety factor still remains above 0.8, significantly higher than the safety threshold (0.5). In contrast, the safety factor of the static path planning method drops sharply and reaches 0.3 at 120 minutes, far lower than the safety threshold; although the GIS-based method is better than the static planning, it also shows an obvious downward trend in the later stage of the disaster.

[0067] Statistical analysis shows that this technical method not only has the highest average safety factor but also the smallest standard deviation (±0.05), indicating its stable dynamic adaptation ability. It can adjust the path planning strategy in a timely manner according to the changes in the disaster environment, effectively improving the safety and reliability of rescue operations. This result fully verifies the significant advantages of the prediction model based on cellular automata in the application of complex disaster scenarios.

[0068] In this embodiment, by dividing the scene into grid cells and mapping key data, the state of each area can be accurately captured, and then the changing trends of factors such as road passing capacity, building damage, hazard source diffusion, and environmental threat level can be dynamically evaluated. This prediction model based on cellular automata can automatically adjust the scene threat level and passing path through iterative calculation after real-time updating of the scene data. Existing technologies usually rely on static models and are difficult to quickly adapt to the changes in the post-disaster scene, and the path planning often cannot reflect the changes in road conditions and threat levels in real time. In existing technologies, path planning mostly relies on simple cost functions without considering the impact of the dynamic environment on the path, resulting in unsatisfactory effects in complex disaster scenarios. In contrast, this solution adjusts the prediction results in real time through a dynamic iterative method and can still provide accurate threat assessment and optimal path selection under the continuous evolution of the disaster. The improvement of this application lies in combining the cellular automata model and the dynamic data update mechanism to achieve continuous prediction and timely adjustment of the scene state. Through the state transition equation and the environmental diffusion model, the evolution of various factors after the disaster can be accurately simulated, ensuring that the system can continuously give the optimal path and threat assessment when the data changes. The improvement of this technology makes the path planning more accurate and the decision support more timely during the rescue process, improving the rescue efficiency and safety.

[0069] In an alternative embodiment, Build a three-dimensional model of the emergency site in the digital twin platform, visually map the scene situation assessment results and the medical resource scheduling plan, overlay the rescue situation annotations in real time through augmented reality technology, and update the location information of rescue personnel and medical equipment in real time based on the target tracking algorithm, including: In the digital twin platform, on-vehicle lidar is used to obtain point cloud data of the emergency site. The point cloud data is registered to obtain a dense point cloud. Based on the region growing algorithm, the dense point cloud is segmented by scene structure to generate a 3D model of the emergency site, and a unified spatial coordinate system is established in the 3D model; Based on the unified spatial coordinate system, the scene threat degree in the scene situation assessment result is mapped to the 3D model to form a situation heat map. The equipment layout data and personnel deployment data in the medical resource scheduling plan are converted into position coordinates in the 3D scene and a situation annotation is generated, obtaining a digital twin scene with situation information; The feature point matching algorithm is used to realize the real-time registration of the mobile device camera and the digital twin scene with situation information, establish the mapping relationship between the camera view and the unified spatial coordinate system, and superimpose and display the situation heat map and situation annotation in the camera view through the augmented reality technology to obtain an augmented reality situation view; Based on the target tracking algorithm, the position information of the medical equipment is obtained in real time, the position information collected by the rescue personnel positioning terminal is received, the position information is coordinate-transformed according to the unified spatial coordinate system, and the transformed position information is superimposed and displayed in the augmented reality situation view based on the mapping relationship; When the scene situation assessment result is updated, a new situation heat map is generated; when the medical resource scheduling plan changes, the situation annotation is updated; when the position information of the rescue personnel and the medical equipment is updated, the position display in the augmented reality situation view is updated in real time based on the mapping relationship.

[0070] Exemplarily, first, on-site point cloud data is collected by on-vehicle lidar. The Velodyne HDL-64E lidar is used to scan at a frequency of 10Hz to obtain high-precision 3D point cloud data. The collected original point cloud data is preprocessed, including noise filtering and outlier removal. The iterative closest point algorithm is used to register multiple frames of point cloud data to generate a high-density point cloud. Based on the region growing algorithm, the point cloud is segmented by scene, and the point cloud data is divided into different categories such as ground, buildings, and vegetation. The triangular mesh reconstruction method is used to generate a 3D scene model, and a unified spatial coordinate system with the center of the accident site as the origin is established.

[0071] The scene situation assessment information is mapped into the 3D model. Based on the deep learning method, the threat degree of the site is evaluated, and the evaluation result is represented by a numerical value from 0 to 1 indicating the degree of danger. The threat degree numerical value is converted into a heat map through color mapping, where red represents a high-risk area and green represents a safe area. At the same time, the equipment layout and personnel deployment information in the medical resource scheduling plan are converted into 3D coordinate points, and corresponding annotation symbols are generated in the model. Different types of medical equipment and rescue personnel use different annotation styles.

[0072] The mobile device captures on-site images in real time through the camera, extracts SIFT feature points, and matches them with the feature points pre-extracted in the 3D model. Based on the RANSAC algorithm, the camera pose is estimated, and the mapping relationship between the camera view and the unified coordinate system is established. The situation heat map and annotation information are superimposed on the camera view through the OpenGL ES rendering engine to achieve the augmented reality display effect.

[0073] The medical device is equipped with a Bluetooth positioning tag and obtains the real-time position through the indoor positioning base station network. The positioning terminal carried by the rescue personnel obtains the position information based on the integrated positioning method of Beidou and inertial navigation. These position data are uniformly converted into the scene coordinate system and are updated and displayed in real time in the augmented reality view. When the position of the device or personnel changes, the system automatically updates its display position in the view.

[0074] The system re-evaluates the scene situation every 30 seconds and updates the heat map display. When the medical resource scheduling plan changes, the corresponding annotation information is updated immediately. The position information is updated at a frequency of 10Hz to ensure the real-time nature of the positions of various elements in the augmented reality view.

[0075] The existing technologies mainly rely on manual observation and simple digital map annotation for emergency rescue command, making it difficult to grasp the on-site situation changes and resource distribution in real time and accurately, and lacking intuitive information display means, which affects the timeliness and accuracy of command decisions. This application proposes a new visualization command method based on digital twin and augmented reality technologies. The on-site point cloud data is collected through vehicle-mounted lidar and an accurate 3D model is established to achieve the accurate mapping of the situation information under the unified spatial coordinate system. By using feature point matching and target tracking algorithms, the scene threat degree heat map, medical resource deployment annotation, and real-time position information of personnel and equipment are superimposed and displayed in the mobile device view, establishing a dynamic association between the real scene and digital information. When the scene situation and resource deployment change, the system can automatically update the display content. This solution overcomes the defects of incomplete information acquisition, non-intuitive display, and untimely update in traditional methods, realizes the real-time perception, three-dimensional presentation, and dynamic update of the on-site situation of emergencies, provides commanders with a more intuitive and accurate situation awareness ability, and significantly improves the scientificity and efficiency of emergency rescue command.

[0076] Optionally, when performing medical resource scheduling, first establish a resource demand model based on the scene situation assessment results. Determine the number and level of ambulances required in each area according to the casualty distribution density and the degree of body temperature abnormality, and give priority to configuring emergency vehicles with rescue functions in the critical care area.

[0077] For medical devices, the system will intelligently match first-aid devices such as ventilators, defibrillators, and monitors according to the injury characteristics reflected by the thermal imaging data, and reasonably plan the layout positions of the devices based on the device transfer radius. In terms of drug dispensing, combined with the body temperature change trend and environmental threat factors, predict the types of possible complications, and reserve targeted drugs in advance.

[0078] For blood transfusion support, the system analyzes the number of wounded and the degree of injury, estimates the types and quantities of blood products required, and formulates a dispensing plan in combination with the inventory situation and transportation time of blood banks. In the dispatching of medical staff, based on the regional danger level and the distribution of the wounded, calculate the allocation ratios of first-aid doctors, nurses, and specialists required, and at the same time consider the matching degree between the professional expertise of medical staff and the types of injuries to achieve precise dispatching. The system will also plan the transfer routes and allocation plans of the wounded according to the bed capacity, specialty characteristics, and treatment capabilities of each hospital to ensure the efficient coordination of medical resources.

[0079] In another alternative embodiment, the following medical resources are allocated through intelligent means: medical transportation tools include ambulances of different levels, medical helicopters, and special transfer vehicles; first-aid devices include portable ventilators, electrocardiogram monitors, defibrillators, infusion pumps, portable B-ultrasounds, mobile DRs, etc.; medical consumables include tracheal intubation, infusion equipment, hemostatic bandages, disposable medical protective supplies, etc.; drug types include cardiopulmonary resuscitation drugs, hemostatic drugs, anti-shock drugs, anti-infection drugs, detoxification drugs, infusion drugs, etc.; blood transfusion products include various types of red blood cells, plasma, platelets, etc.; medical staff include emergency department doctors, intensive care unit doctors, trauma surgeons, nurses, first-aid responders, etc.

[0080] The system analyzes historical case data through deep learning algorithms, establishes a medical resource allocation knowledge base, predicts the resource demand in real time according to the changes in the on-site situation, and generates an optimal allocation plan in combination with the resource reserve situation of surrounding medical institutions.

[0081] During the implementation of the plan, the location and usage status of medical resources are monitored in real time based on the Internet of Things technology. When there is an imbalance between resource supply and demand, the system will automatically trigger an alternative plan to ensure the continuous and orderly progress of the rescue work.

[0082] In the second aspect of the embodiments of the present invention, A first unit is provided for collecting thermal imaging data of the emergency scene through an infrared sensor, extracting the body temperature distribution data and the wounded position data from the thermal imaging data, determining the environmental monitoring range according to the wounded position data, and collecting environmental parameter data within the range through an environmental sensor; The system includes: The second unit is used to construct a body temperature change trend graph based on the body temperature distribution data of the wounded, calculate the value of the degree of body temperature abnormality, calculate the value of the degree of environmental threat based on the environmental parameter data, calculate the regional danger level based on the value of the degree of body temperature abnormality and the value of the degree of environmental threat, count the distribution density of the wounded in combination with the wounded location data, and generate a scene situation assessment result according to the regional danger level and the wounded distribution density; The third unit is used to divide the rescue area according to the wounded distribution density based on the scene situation assessment result, determine the resource allocation weight of each rescue area based on the regional danger level, and generate a medical resource scheduling plan; The fourth unit is used to construct a three-dimensional model of the accident scene in the digital twin platform, perform visual mapping on the scene situation assessment result and the medical resource scheduling plan, overlay rescue situation annotations in real time through augmented reality technology, and update the location information of rescue personnel and medical equipment in real time based on the target tracking algorithm; The fifth unit is used to dynamically adjust the medical resource scheduling plan through a recursive optimization algorithm according to the location information of rescue personnel and medical equipment, and realize the real-time optimal allocation of medical resources.

[0083] In the third aspect of the embodiments of the present invention, there is provided an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0084] In the fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0085] The present invention may be a method, an apparatus, a system, and / or a computer program product. The computer program product may include a computer-readable storage medium, on which computer-readable program instructions for executing various aspects of the present invention are loaded.

[0086] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The emergency medical command method for emergencies based on AI and the Internet of Things is characterized by: include: Collect thermal imaging data at the scene of the emergency through infrared sensors, extract the temperature distribution data and location data of the injured based on the thermal imaging data, determine the environmental monitoring range based on the location data of the injured, and collect environmental parameter data within the range through environmental sensors; Build a temperature change trend chart based on the temperature distribution data of the injured, calculate the abnormal temperature value, and calculate the environmental threat value based on the environmental parameter data. Calculate the regional danger level based on the abnormal temperature value and the environmental threat value, and calculate the distribution density of the injured combined with the location data of the injured. Generate a scenario situation assessment result based on the regional danger level and the distribution density of the injured; Based on the results of the scenario situation assessment, the rescue area is divided according to the density of the wounded, and the resource allocation weight of each rescue area is determined based on the regional danger level to generate a medical resource scheduling plan; Build a three-dimensional model of the emergency scene in the digital twin platform, visualize the scene situation assessment results and medical resource scheduling plan, overlay rescue situation annotations in real time through augmented reality technology, and update the rescue personnel location information and medical equipment location information in real time based on the target tracking algorithm; According to the location information of rescuers and medical equipment, the medical resource scheduling plan is dynamically adjusted through a recursive optimization algorithm to achieve real-time optimal allocation of medical resources.

2. The method according to claim 1, characterized in that The infrared sensor is used to collect thermal imaging data at the scene of the emergency, and the temperature distribution data and location data of the injured are extracted based on the thermal imaging data, including: Collect thermal imaging data of the scene of the emergency by infrared sensors, and perform adaptive histogram equalization enhancement processing on the thermal imaging data to obtain enhanced thermal imaging data, determine the size of the filter window based on the local temperature variance of the enhanced thermal imaging data, and use an adaptive median filtering algorithm to reduce noise on the enhanced thermal imaging data to obtain reduced-noise thermal imaging data; A mapping function between grayscale value and actual temperature is established based on the temperature response characteristics of the infrared sensor, and the grayscale value in the denoised thermal imaging data is converted into a temperature value to generate a temperature distribution map. A dual-threshold detection interval is set based on the normal body temperature range of the human body, and the pixel points in the temperature distribution map whose temperature values ​​fall within the dual-threshold detection interval are subjected to regional connectivity analysis to obtain candidate injured areas. Perform morphological processing on the candidate injured area to extract the regional contour features, area features and aspect ratio features, and screen out the areas that do not meet the features according to the human body size feature template to obtain the confirmed injured area; A temperature field distribution model for the confirmed injured area is established, and the temperature numerical sequence, temperature gradient distribution and temperature uniformity characteristics in the area are extracted as the injured body temperature distribution data; the minimum circumscribed rectangle is calculated based on the contour characteristics of the confirmed injured area, and the coordinates of the center point of the rectangle are used as the injured position data.

3. The method according to claim 1, characterized in that According to the temperature distribution data of the injured, a temperature change trend chart is constructed, and the abnormal temperature value is calculated. At the same time, the environmental threat value is calculated according to the environmental parameter data. The regional danger level is calculated based on the abnormal temperature value and the environmental threat value. The distribution density of the injured is calculated in combination with the location data of the injured. The scene situation assessment results generated according to the regional danger level and the distribution density of the injured include: Establish a temperature monitoring window, divide the temperature monitoring window into an observation interval and a prediction interval, perform segmented fitting on the temperature data in the observation interval to extract the fluctuation law, establish a temperature baseline based on the prediction interval, calculate the degree to which the fluctuation law deviates from the temperature baseline to obtain the fluctuation abnormality feature, and determine the temperature abnormality degree value according to the fluctuation abnormality feature; According to the environmental parameter data, an influence network including environmental parameters is constructed, the leading abnormal parameters are taken as root nodes, the affected parameters are taken as child nodes, a directed graph structure of the transmission relationship between parameters is established, the abnormal diffusion path is analyzed based on the directed graph structure, and the environmental threat degree value is calculated in combination with the range and speed of abnormal diffusion; According to the change trends of the abnormal body temperature degree value and the environmental threat degree value, dynamically update the inference rules of the neural network, and use the inference rules to calculate the regional danger level; The spatial distribution of the injured position data is analyzed, the movement trajectory of the injured is established, the intersection points of the trajectories are extracted as key nodes, and the concentration of personnel at each key node is calculated to obtain the distribution density of the injured; The danger level of the area is used as a weight coefficient, and the density of the distribution of the wounded is weightedly calculated to obtain a scene situation assessment result.

4. The method according to claim 3, characterized in that The spatial distribution of the injured position data is analyzed, the movement trajectory of the injured is established, the intersection points of the trajectories are extracted as key nodes, and the concentration of personnel at each key node is calculated to obtain the distribution density of the injured, including: The position data of the injured are segmented according to the time sequence to obtain a time window sequence, the change of the position coordinates in the time window sequence is calculated to obtain a displacement vector, the displacement vectors are connected in time sequence to construct a movement trajectory diagram of the injured, the movement trajectory diagram of the injured is smoothed to eliminate abnormal jumps, and the target movement path is extracted according to the smoothed trajectory; Draw the target moving path in the spatial coordinate system, calculate the intersection angle and the shortest distance between the paths, determine the track intersection point when the intersection angle and the shortest distance meet the preset conditions, count the intersection frequency at each track intersection point, calculate the number of people per unit area around the track intersection point to obtain the degree of people gathering, and determine the key node according to the weighted value of the intersection frequency and the degree of people gathering; A local coordinate system is established with the key node as the center, the cross frequency and the degree of personnel gathering are mapped to the size of the influence range and the attenuation rate respectively, a node influence field is constructed according to the influence range and the attenuation rate, and multiple node influence fields are superimposed to generate a distribution influence diagram; The real-time change characteristics of the injured person's movement trajectory diagram are converted into dynamic weights, and the distribution influence diagram is dynamically weighted to obtain a density distribution model; The target area is divided into grid units, the distance between the center of each grid unit and the key node is calculated, the baseline density value in each grid unit is calculated according to the distance and the density distribution model, and the time-varying characteristics of the dynamic weight are superimposed on the baseline density value to obtain the casualty distribution density reflecting the dynamic distribution of personnel.

5. The method according to claim 1, characterized in that Based on the results of the scenario situation assessment, the rescue area is divided according to the density of the casualties, and the resource allocation weight of each rescue area is determined based on the regional danger level. The medical resource scheduling plan is generated, including: Receive the scene situation assessment results and casualty distribution density data, build a prediction model based on cellular automation, analyze the changing trend of the scene situation, and obtain the scene threat level and navigable path of each area at the future moment; Calculate the density difference of adjacent areas according to the density data of the casualty distribution, mark the areas where the density difference is less than the density threshold and the spatial distance is less than the distance threshold as areas to be merged, optimize the boundaries of the areas to be merged based on the threat level of the scene, and generate rescue partitions; Based on the correspondence between the amount of medical resources invested and the treatment effect of the wounded in the historical rescue data, the resource utilization efficiency of each rescue zone is calculated, and the regional danger level is calculated according to the distribution density of the wounded in the rescue zone, the threat level of the scene and the resource utilization efficiency; A resource transfer network is constructed with rescue zones as nodes and traversable paths as edges, resource allocation weights are obtained by weighted calculation of the regional danger level and resource utilization efficiency, and a medical resource scheduling plan with minimum transfer loss is generated based on the resource transfer network; Monitor changes in scenario situation assessment results. When the scenario threat level changes beyond the situation threshold, redivide the rescue zones based on the updated scenario threat level and casualty distribution density, adjust the regional danger level and resource allocation weights, and update the medical resource scheduling plan.

6. The method according to claim 5, characterized in that Based on the cellular automation, the prediction model is constructed to analyze the changing trend of the scene situation, and the scene threat degree and navigable paths of each area at the future moment are obtained, including: The rescue scene is divided into regular grid cells, and the road traffic status, building damage degree, hazard source intensity and environmental threat degree in the scene situation data are mapped to each grid cell to form the initial state matrix of the grid cell; The evolution value of road traffic state is calculated according to the frequency of rescue vehicle traffic, the evolution value of building damage degree is calculated according to the secondary disaster monitoring data, and the diffusion value of hazard source intensity and environmental threat degree between adjacent grid cells is calculated. The evolution value of road traffic state, the evolution value of building damage degree, the diffusion value of hazard source intensity and environmental threat degree are constructed into a state transfer equation; The state transfer equation is solved by iterative calculation to obtain the state matrix of each grid cell at the prediction time, and the scene threat degree at the prediction time is calculated according to the state matrix; The pass probability is calculated based on the road traffic status and scene threat level of the grid cells, the path cost function is constructed and the search algorithm is used to solve the passable path; Receive scene situation data in real time. When the data is updated, map the updated data into a new initial state matrix, re-execute the prediction calculation, and dynamically update the scene threat level and navigable path.

7. The method according to claim 1, characterized in that A three-dimensional model of the emergency scene is constructed in the digital twin platform, and the scene situation assessment results and medical resource dispatch plan are visualized and mapped. The rescue situation annotation is superimposed in real time through augmented reality technology, and the location information of rescuers and medical equipment is updated in real time based on the target tracking algorithm, including: In the digital twin platform, a vehicle-mounted laser radar is used to obtain point cloud data of the emergency scene, the point cloud data is registered to obtain a dense point cloud, the dense point cloud is segmented into scene structures based on a region growing algorithm, a three-dimensional model of the emergency scene is generated, and a unified spatial coordinate system is established in the three-dimensional model; Based on the unified spatial coordinate system, the scene threat degree in the scene situation assessment results is mapped to the three-dimensional model to form a situation heat map, and the equipment layout data and personnel deployment data in the medical resource scheduling plan are converted into position coordinates in the three-dimensional scene and generate situation annotations to obtain a digital twin scene with situation information; A feature point matching algorithm is used to achieve real-time registration between the mobile device camera and the digital twin scene with situation information, and a mapping relationship between the camera view and the unified spatial coordinate system is established. The situation heat map and situation annotation are superimposed and displayed in the camera view through augmented reality technology to obtain an augmented reality situation view. Acquire the location information of the medical device in real time based on the target tracking algorithm, receive the location information collected by the rescuer positioning terminal, convert the location information according to the unified space coordinate system, and overlay the converted location information on the augmented reality situation view based on the mapping relationship; When the scene situation assessment results are updated, the situation heat map is regenerated; when the medical resource scheduling plan changes, the situation annotation is updated; when the rescue personnel location information and medical equipment location information are updated, the location display in the augmented reality situation view is updated in real time based on the mapping relationship.

8. An emergency medical command system based on AI and the Internet of Things, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to collect thermal imaging data at the scene of the emergency through an infrared sensor, extract the body temperature distribution data and the position data of the injured according to the thermal imaging data, determine the environmental monitoring range according to the position data of the injured, and collect environmental parameter data within the range through the environmental sensor; The second unit is used to construct a temperature change trend chart based on the temperature distribution data of the injured, calculate the abnormal temperature value, and calculate the environmental threat value based on the environmental parameter data. The regional danger level is calculated based on the abnormal temperature value and the environmental threat value, and the distribution density of the injured is calculated in combination with the location data of the injured. The scene situation assessment result is generated according to the regional danger level and the distribution density of the injured; The third unit is used to divide the rescue area according to the density of the wounded distribution based on the scene situation assessment results, determine the resource allocation weight of each rescue area based on the regional danger level, and generate a medical resource scheduling plan; The fourth unit is used to build a three-dimensional model of the emergency scene in the digital twin platform, visualize the scene situation assessment results and the medical resource scheduling plan, overlay the rescue situation annotation in real time through augmented reality technology, and update the rescue personnel location information and medical equipment location information in real time based on the target tracking algorithm; The fifth unit is used to dynamically adjust the medical resource scheduling plan according to the rescue personnel location information and medical equipment location information through a recursive optimization algorithm to achieve real-time optimal allocation of medical resources.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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