Dynamic route planning method of emergency system

Abstract

The invention provides a dynamic route planning method of a first-aid system, which comprises the following steps: acquiring a first-aid request data set including vital signs of a patient, position coordinates and a real-time position of a first-aid vehicle; collecting dynamic traffic data in real time, wherein the dynamic traffic data comprises a passing speed, a traffic control area, an accident point coordinate and a severity level; medical institution state data are obtained, wherein the medical institution state data comprise the reception capacity, the first-aid channel occupation state and the resource matching degree parameter; based on the above data, an initial path set is calculated through an improved dynamic modulation factor, and a three-dimensional demodulation matrix is used to carry out priority ranking on the paths. A final navigation path is generated through multi-level logic judgment, and fuel quantity judgment, accident point influence updating and medical institution state calibration are included. And finally, correcting the dynamic modulation factor according to the path execution time deviation. The first-aid efficiency and the success rate can be improved, and precious treatment time is won for patients.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation and medical emergency, and more specifically, to a dynamic route planning method for an emergency system. Background Art

[0002] In modern cities, the efficient operation of the emergency system is crucial to saving lives. Existing emergency route planning methods mainly rely on static map information and simple shortest path algorithms. Although they can meet basic navigation needs to a certain extent, they cannot fully consider dynamic factors such as real-time traffic conditions, accident risks, medical institution resource status, and patient vital signs. This single planning method often leads to problems such as extended emergency vehicle travel time and improper resource matching when facing complex urban traffic environments and emergency medical needs, thus affecting the efficiency and success rate of emergency treatment.

[0003] With the development of intelligent transportation technology, dynamic traffic information collection and processing technology has gradually matured, providing the possibility of optimizing emergency route planning. However, most of the existing dynamic route planning methods only focus on the real-time update of traffic conditions, ignoring key factors such as the urgency of the patient's condition in the emergency mission, the safety risk of the accident site, and the reception capacity of medical institutions. The lack of these factors makes it impossible for the existing system to achieve truly intelligent emergency route planning.

[0004] There are at least the following problems or defects in the existing technology: the existing emergency route planning method is unable to integrate multi-dimensional data such as dynamic traffic data, accident risk information, medical institution resource status, and patient vital signs in real time, resulting in insufficient path planning accuracy and unable to effectively respond to complex and changeable emergency scenarios, thereby affecting emergency efficiency and patient treatment success rate. Summary of the invention

[0005] The present invention provides a dynamic route planning method for an emergency system, comprising: S1. Obtaining a first aid request data set, the data set includes patient vital sign parameters , patient location coordinates , emergency vehicle real-time location coordinates ; S2. Real-time collection of dynamic traffic data sets in the target area, including the speed of each road section , traffic control area coordinate set , Real-time accident point coordinate queue The severity of the accident ; S3. Obtain medical institution status dataset, including emergency department admission capacity , Emergency channel occupancy status , Medical resource matching parameters ; S4. Based on the emergency request data set , , in the dynamic traffic dataset , , and the , , through the improved dynamic modulation factor Calculate the initial path set ,in Including time dimension weight coefficient and spatial dimension weight coefficient ; S5. Using the 3D demodulation matrix For the initial path set Prioritize the three-dimensional demodulation matrix The dimensions include: path safety parameters calculated based on real-time accident point data , based on the road segment speed Timeliness parameters , based on medical resource matching parameters and emergency department capacity Resource matching parameters ; S6. Execute multi-level logical judgment to generate the final navigation path , specifically including: S61. According to the fuel level of the emergency vehicle With threshold The comparison result triggers path recalculation; S62. Based on real-time accident point queue In Update the speed of the road section ; S63. Combined with the medical institution status data set and Calibrated Path Scoring ; S7. Execution time deviation according to path Corrected dynamic modulation factor .

[0006] Furthermore, the improved dynamic modulation factor calculation in step S4 includes: S41. Constructing space-time coupling function: , in, represents the traffic flow change rate calculated based on the dynamic traffic data set, represents the spatial complexity of the calculation based on the road topology, is the node spacing, is the regional characteristic distance, represents the weather impact factor calculated based on meteorological data, is the rainfall intensity, , , is the normalized weight coefficient; S42. Calculate the dynamic weight of the road section: ,in, is the actual distance from node i to node j, is the medical resource impact coefficient defined based on the medical institution status dataset, The emergency channel is occupied. is the original road section speed; S43. Generate dynamic adjacency matrix ,in is the accident point attenuation factor, Indicates The impact of each accident point is attenuated. is the time decay constant, For accident point The time of occurrence.

[0007] Furthermore, the three-dimensional demodulation matrix construction in step S5 includes: S51. Calculate path security dimension parameters: ,in, The road segment is calculated based on historical accident data Safety weight, is the aggregated impact of the accident point, For road section The associated accident point set; S52. Calculate the timeliness dimension parameters: ,in, is the total path length, Based on dynamic traffic data set Calculate the weighted average speed, For road section length, is the velocity fluctuation; S53. Calculate resource matching dimension parameters: ,in, The specialist match is calculated based on the patient's symptoms. Provide medical resources needed for the patient’s condition. is the weight of symptom s, To meet resource requirements, is the capacity reference constant.

[0008] Further, step S61 includes: S611. When Based on the real-time location of the emergency vehicle and gas station coordinates in the dynamic traffic dataset , generate a set of alternative paths ; S612. Based on the scoring model right Reorder; S613. Choose to satisfy and The path as ,in The preset safety threshold.

[0009] Further, step S62 includes: S621. For each accident point , based on its severity level Calculate the influence radius ,in From real-time incident point queue data; S622. Update the road section speed: ,in, is the shortest distance between road section k and accident point a, is the set of accident points affecting road section k; S623. When there is , delete the corresponding road segment and generate a new adjacency matrix .

[0010] Further, step S63 includes: S631. Based on the medical institution status data set and , calculate the load factor ; S632. Dynamically adjust resource matching weights: ,in ; S633. Generate calibrated path score: .

[0011] Further, step S7 includes: S71. Record the execution time deviation of each path ; S72. Calculate the modulation factor correction: ,in, is the standard deviation of the deviation distribution; S73. Update dynamic modulation factor: , is the preset time base constant.

[0012] Furthermore, the medical resource matching parameter The calculation includes: S81. Construct a disease-resource mapping matrix ,in represents disease i requiring resource j, It means that disease i does not require resource j; S82. Based on patient vital signs parameters Generate symptom vector , Indicates the severity of symptom k; S83. Based on matrix D and vector Generate matching parameters: ,in, is the capacity of resource j, is the occupancy of resource j.

[0013] Furthermore, the security weight The calculation includes: S91. Obtain the historical accident frequency of road section k , is the number of accidents during period t; S92. Calculate road condition coefficient , Score lane width, Score the lighting; S93. Generate dynamic security weight: , Set speed limit for the road section.

[0014] Furthermore, the degree of specialty matching The calculation includes: S101. Extract the main symptoms of patients ; S102. Query the mapping table to obtain , Specialized configuration for medical institutions; S103. Calculate dynamic matching degree: , To delay time, is the disease half-life parameter.

[0015] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the dynamic route planning method of the emergency system of the present invention can comprehensively consider multi-dimensional factors such as patient vital signs, real-time traffic conditions, accident risks, and resource status of medical institutions, and dynamically optimize and prioritize the path through improved dynamic modulation factors and three-dimensional demodulation matrices. This method can dynamically adjust the path planning according to real-time data to ensure that the emergency vehicle selects the optimal path in a complex urban traffic environment, thereby effectively shortening the emergency response time and improving the efficiency of emergency.

[0016] In addition, the present invention can also calibrate the path score and correct the dynamic modulation factor in real time through a multi-level logic judgment mechanism, further improving the adaptability and accuracy of path planning. Combining the medical institution status data and the patient's condition needs, this method can achieve accurate matching of resources, ensuring that the patient is sent to the most suitable medical institution in the shortest time, thereby improving the success rate of emergency treatment and the quality of patient treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A schematic flow chart of a dynamic route planning method for an emergency rescue system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0019] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0020] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0021] Reference below Figure 1 , Figure 1 The following is a flow chart of a dynamic route planning method for an emergency rescue system according to an embodiment of the present invention. Figure 1 As shown, a dynamic route planning method for an emergency system includes: S1. Obtaining a first aid request data set, the data set includes patient vital sign parameters , patient location coordinates , emergency vehicle real-time location coordinates ; S2. Real-time collection of dynamic traffic data sets in the target area, including the speed of each road section Traffic control area coordinate set , Real-time accident point coordinate queue The severity of the accident ; S3. Obtain medical institution status dataset, including emergency department admission capacity , Emergency channel occupancy status , Medical resource matching parameters ; S4. Based on the emergency request data set , , in the dynamic traffic dataset , , and the , , through the improved dynamic modulation factor Calculate the initial path set ,in Including time dimension weight coefficient and spatial dimension weight coefficient ; S5. Using the 3D demodulation matrix right Prioritize, 3D demodulation matrix The dimensions include: path safety parameters calculated based on real-time accident point data , based on the road segment speed Timeliness parameters ,based on and Resource matching parameters ; S6. Execute multi-level logical judgment to generate the final navigation path , specifically including: S61. According to the fuel level of the emergency vehicle With threshold The comparison result triggers path recalculation; S62. Based on real-time accident point queue In Update the speed of the road section ; S63. Combined with the medical institution status data set and Calibrated Path Scoring ; S7. Execution time deviation according to path Corrected dynamic modulation factor .

[0022] It should be noted that the dynamic route planning method for the emergency system of the present invention optimizes and sorts the path by obtaining the emergency request data set, the dynamic traffic data set and the medical institution status data set, combining the improved dynamic modulation factor and the three-dimensional demodulation matrix. Among them, the emergency request data set includes the patient's vital sign parameters, the patient's location coordinates and the real-time location coordinates of the emergency vehicle, which are the basic information for path planning. The dynamic traffic data set covers the speed of each section of the road, the traffic control area coordinate set, the real-time accident point coordinate queue and the accident severity level, which can reflect the dynamic changes of the current traffic conditions. The medical institution status data set includes the emergency department's reception capacity, the occupancy status of the emergency channel and the medical resource matching parameters, which are used to evaluate the receiving capacity of the target medical institution. Through the comprehensive analysis of these data, the dynamic planning of the emergency path can be realized to ensure that the emergency vehicle can quickly and safely send the patient to the most suitable medical institution.

[0023] Specifically, the patient vital sign parameters in the emergency request data set refer to the patient's current health status indicators, such as heart rate, blood pressure, respiratory rate, etc. These parameters can reflect the urgency of the patient's condition. The patient's location coordinates and the real-time location coordinates of the emergency vehicle are obtained through the GPS positioning system to determine the relative position relationship between the emergency vehicle and the patient for rapid response. The traffic speed in the dynamic traffic data set can be obtained in real time through traffic sensors or vehicle driving data, the traffic control area coordinate set is derived from the control information released by the traffic management department, and the real-time accident point coordinate queue and its accident severity level are obtained through the traffic monitoring system or the accident alarm system. The emergency department reception capacity in the medical institution status data set refers to the number of patients that the medical institution can receive within a certain period of time. The emergency channel occupancy status is used to determine whether the emergency vehicle can enter the medical institution smoothly. The medical resource matching degree parameter is calculated based on the patient's condition and the resource allocation of the medical institution. The specific settings of these parameters can be adjusted according to the actual situation. For example, the reception capacity can be set according to the scale and department configuration of the medical institution, and the medical resource matching degree parameter can be calculated based on the patient's main symptoms and the specialty configuration of the medical institution.

[0024] Preferably, in order to further optimize the path planning process, the collection of road construction information can be increased when acquiring the dynamic traffic data set so as to more comprehensively reflect the traffic conditions. When calculating the improved dynamic modulation factor, meteorological data, such as rainfall intensity, can be introduced to evaluate the impact of weather on traffic and emergency tasks. In addition, when generating the initial path set, the paths can be prioritized according to the urgency of the patient's condition to ensure that critically ill patients have priority access to emergency resources. In the medical institution status data set, the calculation method of the medical resource matching parameters can be further refined, for example, a more accurate match can be made based on the patient's specific symptoms and the number of specialists, equipment resources, etc. of the medical institution. Through these refinement and optimization measures, the efficiency and reliability of the emergency system can be further improved to ensure that patients receive effective treatment in the shortest time.

[0025] In some embodiments, the improved dynamic modulation factor calculation in step S4 includes: S41. Constructing space-time coupling function: in, represents the traffic flow change rate calculated based on the dynamic traffic data set, represents the spatial complexity of the calculation based on the road topology, is the node spacing, is the regional characteristic distance, represents the weather impact factor calculated based on meteorological data, is the rainfall intensity, is the normalized weight coefficient; S42. Calculate the dynamic weight of the road section: in, For Node arrive The actual distance is the medical resource impact coefficient defined based on the medical institution status dataset, The emergency channel is occupied; S43. Generate dynamic adjacency matrix ,in is the accident point attenuation factor, Indicates The impact of each accident point is attenuated. is the time decay constant, For accident point The time of occurrence.

[0026] It should be noted that the calculation of the improved dynamic modulation factor in the present invention is a key link in realizing dynamic route planning. By constructing a spatiotemporal coupling function and combining the traffic flow change rate, spatial complexity and weather influencing factors, the influence of time and space factors on path planning can be comprehensively considered. Among them, the traffic flow change rate reflects the dynamic changes in traffic flow in each section of the dynamic traffic data set; the spatial complexity is calculated based on the road topology structure, reflecting the complexity of the road network; the weather influencing factor considers the impact of meteorological conditions on the driving of emergency vehicles. Through the comprehensive calculation of these factors, a more adaptive dynamic modulation factor can be obtained, thereby providing more accurate weight guidance for path planning.

[0027] Specifically, the traffic flow change rate is obtained by differential calculation of the flow data of each road section in the dynamic traffic data set, which reflects the change trend of flow over time. The spatial complexity is calculated by the node spacing and regional characteristic distance in the road topology structure, where the node spacing represents the distance between adjacent nodes in the road network, and the regional characteristic distance is a reference value set according to the scale of the entire target area and the road distribution. The weather impact factor takes into account meteorological parameters such as rainfall intensity, and calculates its influence on path planning through an exponential decay function. When calculating the dynamic weight of the road section, the actual distance between nodes, the medical resource impact coefficient, and the emergency channel occupancy status are combined. These parameters jointly determine the priority of the road section. The generation of the dynamic adjacency matrix takes into account the accident point attenuation factor, which is used to evaluate the impact of the accident on the surrounding sections, thereby providing a more accurate adjacency relationship for path planning.

[0028] Preferably, when constructing the spatiotemporal coupling function, the normalized weight coefficient can be adjusted according to the actual application scenario to balance the influence of the time dimension and the space dimension. For example, in areas with severe traffic congestion, the weight of the traffic flow change rate can be appropriately increased; in complex urban road networks, the weight of spatial complexity can be increased. In addition, for weather influencing factors, in addition to rainfall intensity, other meteorological parameters such as visibility or wind speed can be introduced to more comprehensively reflect the impact of meteorological conditions on emergency rescue tasks. When calculating the dynamic weight of a road section, the calculation method of the medical resource impact coefficient can be further refined, such as dynamically adjusting according to the patient's specific condition and the type of resources required. Through these refinement and optimization measures, the adaptability of the dynamic modulation factor and the accuracy of path planning can be further improved.

[0029] In some embodiments, the three-dimensional demodulation matrix construction in step S5 includes: S51. Calculate path security dimension parameters: in, The road segment is calculated based on historical accident data Safety weight, is the aggregated impact of the accident point, For road section The associated accident point set; S52. Calculate the timeliness dimension parameters: in, is the total path length, Based on dynamic traffic data set Calculate the weighted average speed, For road section length, is the velocity fluctuation; S53. Calculate resource matching dimension parameters: in, The specialist match is calculated based on the patient's symptoms. Provide medical resources needed for the patient’s condition. Symptoms The weight of To meet resource requirements, is the capacity reference constant.

[0030] It should be noted that the construction of the three-dimensional demodulation matrix in the present invention is an important part of realizing path priority sorting. By calculating the parameters of the three dimensions of path safety, timeliness and resource matching, the initial path set can be comprehensively evaluated and sorted. Among them, the path safety parameter reflects the level of accident risk on the path; the timeliness parameter takes into account the travel speed and speed fluctuation of the path; the resource matching parameter evaluates the degree of match between the resources of the medical institution and the patient's needs. Through comprehensive consideration of these three dimensions, the optimal path can be selected for the emergency vehicle to ensure that the patient is sent to the most suitable medical institution in the shortest time.

[0031] Specifically, the calculation of path safety dimension parameters is based on historical accident data and real-time accident point information. The road section safety weight is calculated based on the historical accident frequency and road condition coefficient, reflecting the safety level of the road section under normal circumstances. The accident point aggregation impact takes into account the impact of real-time accident points on the path, and calculates its dynamic impact on surrounding sections through the accident point attenuation function. The calculation of timeliness dimension parameters involves the total length of the path, the weighted average speed and the speed fluctuation. The weighted average speed reflects the actual traffic efficiency of each section on the path, and the speed fluctuation is used to evaluate the stability of the path. The resource matching dimension parameters combine the medical resources required for the patient's condition and the resource status of the medical institution, and reflect the availability of resources by dynamically adjusting the matching degree. The specific settings of these parameters can be adjusted according to actual traffic and medical data to adapt to different application scenarios.

[0032] Preferably, when calculating the path safety parameters, the accident point attenuation function can be further refined, and more factors (such as accident type, accident handling progress, etc.) can be introduced to more accurately evaluate the impact of the accident on the path. For timeliness parameters, it is possible to consider introducing real-time traffic flow data and dynamically adjusting the calculation method of the weighted average speed to more accurately reflect the traffic efficiency of the path. In the calculation of resource matching parameters, in addition to considering the patient's condition and the resource status of the medical institution, the time factor can also be introduced, such as dynamically adjusting the resource matching weight according to the urgency of the patient's condition and the estimated arrival time. In addition, for the specialist matching degree in the resource matching dimension parameters, a more detailed calculation can be performed in combination with the patient's main symptoms and the specialist configuration of the medical institution, such as by introducing a disease-resource mapping matrix to achieve a more accurate match. Through these refinement and optimization measures, the accuracy and practicality of the three-dimensional demodulation matrix can be further improved, thereby providing a more reliable basis for emergency path planning.

[0033] In some embodiments, step S61 includes: S611. When Based on the real-time location of the emergency vehicle and gas station coordinates in the dynamic traffic dataset , generate a set of alternative paths ; S612. Based on the scoring model right Reorder; S613. Choose to satisfy and The path as ,in The preset safety threshold.

[0034] It should be noted that the present invention introduces a fuel quantity judgment mechanism in the path planning process to ensure that the emergency vehicle will not interrupt its journey due to lack of fuel when performing its mission. When the fuel quantity of the emergency vehicle is lower than the preset threshold, the system will generate a set of alternative paths based on the current location of the vehicle and dynamic traffic data. These paths will guide the vehicle to the nearest gas station. The alternative paths are sorted through the scoring model, and a path that meets both safety requirements and efficiency is selected as the final navigation path. This mechanism effectively avoids delays caused by lack of fuel and ensures the continuity and reliability of emergency missions.

[0035] Specifically, the core of the fuel quantity judgment mechanism is to compare the actual fuel quantity of the emergency vehicle with the preset safety threshold. When the fuel quantity is lower than the threshold, the system will start the path recalculation process. The generation of the alternative path set is based on the real-time location of the emergency vehicle and the coordinates of the gas station in the dynamic traffic data. The alternative path set is constructed by calculating the reachable path between the current location of the vehicle and the gas station. The scoring model comprehensively considers the safety and timeliness of the path, with the safety weight of 0.7 and the timeliness weight of 0.3. This weight allocation strategy reflects the principle of giving priority to path safety in emergency situations. The setting of the safety threshold is based on the estimation of the vehicle's fuel consumption rate and the task completion time to ensure that the vehicle has enough fuel to maintain operation before arriving at the gas station.

[0036] Preferably, when generating a set of alternative routes, the real-time operating status of the gas station can be further considered, for example, by connecting with the gas station's management system to obtain information on whether it is open for business, thereby avoiding directing vehicles to stations that cannot be refueled. In addition, the weights in the scoring model can be adjusted according to actual needs. For example, in some special cases, the importance of timeliness may be higher than safety. At this time, the weight of timeliness can be appropriately increased. At the same time, in order to improve the flexibility of path planning, consideration of backup gas stations can be added to the set of alternative routes. When the nearest gas station cannot meet the demand, the system can automatically switch to the suboptimal path. Through these refinement and optimization measures, the practicality and reliability of the fuel quantity judgment mechanism can be further improved to ensure that emergency vehicles can successfully complete their tasks under any circumstances.

[0037] In some embodiments, step S62 includes: S621. For each accident point , based on its severity level Calculate the influence radius ,in From real-time incident point queue data; S622. Update the road section speed: ,in, is the shortest distance between road section k and accident point a, is the set of accident points affecting road section k; S623. When there is , delete the corresponding road segment and generate a new adjacency matrix .

[0038] It should be noted that the present invention responds to the impact of real-time accident points by dynamically updating the speed of road sections to ensure the accuracy and timeliness of path planning. Specifically, the system calculates the impact radius of the accident point according to the severity level of the accident point, and adjusts the speed of the affected road section accordingly. This process involves the calculation of the distance between the accident point and the road section and the dynamic adjustment of the speed, so that the impact of the accident on the traffic flow is fully considered in the path planning, avoiding delays in emergency tasks caused by traffic congestion caused by the accident.

[0039] Specifically, the severity level of the accident point is divided according to the real-time accident data, usually divided into 1 to 3 levels, where 1 indicates a minor accident and 3 indicates a serious accident. Based on the severity level of the accident, the system calculates the impact radius of the accident point, that is, the potential impact range of the accident on the traffic capacity of surrounding roads. The update of the traffic speed is achieved by considering the closest distance between the accident point and the road section, combined with the impact attenuation function. The attenuation function reflects the impact of the accident on sections of different distances. The closer the section is to the accident point, the greater the adjustment of the traffic speed. When the updated section traffic speed is lower than the preset minimum threshold, the system will remove the section from the path planning and regenerate the adjacency matrix to ensure that the path planning avoids severely congested areas.

[0040] Preferably, in order to more accurately evaluate the impact of accidents on traffic speed, more accident characteristic parameters can be introduced, such as accident type (such as collision, rollover, etc.) and accident handling progress. These parameters can further refine the calculation of the impact radius and attenuation function. In addition, for the update of traffic speed, the parameters of the attenuation function can be dynamically adjusted in combination with real-time traffic flow data to make it closer to the actual traffic conditions. For example, during peak traffic hours, the impact of accidents on traffic speed may be more significant, and the attenuation coefficient can be appropriately increased. At the same time, the system can also introduce historical accident data to model the impact of different types of accidents, so that the traffic speed can be adjusted more quickly and accurately when facing new accidents. Through these optimization measures, the response capability of path planning to accidents can be further improved, ensuring that emergency vehicles can avoid congestion and reach their destination quickly.

[0041] In some embodiments, step S63 includes: S631. Based on the medical institution status data set and , calculate the load factor ; S632. Dynamically adjust resource matching weights: ,in ; S633. Generate calibrated path score: .

[0042] It should be noted that the present invention calibrates the path score by combining the medical institution status data to ensure that the path planning not only takes into account the traffic conditions and accident risks, but also fully combines the actual receiving capacity of the medical institution. Specifically, the system will dynamically adjust the resource matching weight according to the load of the medical institution, and generate a calibrated path score accordingly. This process involves the calculation of the load factor of the medical institution and the dynamic adjustment of the resource matching weight, so that those medical institutions that can quickly receive and treat patients are given priority in path planning, further improving the overall efficiency of the emergency system.

[0043] Specifically, the load factor in the medical institution status data is calculated by comprehensively considering the emergency department's admission capacity and the emergency channel occupancy status, and is used to reflect the current reception capacity of the medical institution. In the calculation formula of the load factor, the admission capacity indicates the number of patients that the medical institution can receive within a certain period of time, and the emergency channel occupancy status reflects the difficulty of emergency vehicles entering the hospital. The dynamic adjustment of the resource matching weight is based on the size of the load factor and is achieved by introducing nonlinear functions (such as exponential decay functions). When the load factor is high, it means that the medical institution is close to saturation, and the resource matching weight will be reduced accordingly, thereby reducing the priority of the medical institution in the path score. The calibrated path score comprehensively considers the safety, timeliness and resource matching weight of the path, and obtains the final score by weighted summation, which is used to guide emergency vehicles to choose the optimal path.

[0044] Preferably, when calculating the load factor, more status parameters of medical institutions can be introduced, such as the occupancy rate of operating rooms, the utilization rate of intensive care unit (ICU) beds, etc. These parameters can more comprehensively reflect the receiving capacity of medical institutions. At the same time, the adjustment function of the resource matching weight can be fitted in combination with actual data, for example, by analyzing the relationship between the success rate of historical emergency tasks and the load of medical institutions, the parameters of the weight adjustment function can be optimized. In addition, the time factor can be introduced to update the load factor and resource matching weight in real time according to the dynamic changes of the estimated arrival time of the emergency vehicle and the medical institution. For example, if the estimated arrival time is long, the weight of the load factor can be appropriately reduced because the medical institution may release some resources during this period. Through these refinement and optimization measures, the accuracy and practicality of the path score can be further improved, ensuring that emergency vehicles can quickly and efficiently send patients to the most suitable medical institutions.

[0045] In some embodiments, step S7 includes: S71. Record the execution time deviation of each path ; S72. Calculate the modulation factor correction: ,in, is the standard deviation of the deviation distribution; S73. Update dynamic modulation factor: , is the preset time base constant.

[0046] It should be noted that the present invention realizes adaptive optimization of path planning by recording the path execution time deviation and correcting the dynamic modulation factor. The path execution time deviation refers to the difference between the actual driving time and the planned time. This deviation reflects the degree of mismatch between the path planning and the actual traffic conditions. The system uses this deviation to correct the dynamic modulation factor, which is a key parameter in path planning and is used to adjust the path weight to make it more in line with actual traffic conditions. By correcting the dynamic modulation factor, the system can continuously learn and adapt to changes in the traffic environment, thereby improving the accuracy and reliability of path planning.

[0047] Specifically, the path execution time deviation is recorded by comparing the actual arrival time of the emergency vehicle with the estimated arrival time in the path planning. The standard deviation of the deviation distribution is used to measure the discreteness of the deviation, which reflects the fluctuation of the path execution time deviation. The correction amount of the dynamic modulation factor is calculated based on these deviation data. By introducing the standard deviation of the deviation distribution and the preset time reference constant, the system can quantify the impact of the deviation on the modulation factor. The corrected dynamic modulation factor will be adjusted according to the size and direction of the deviation. For example, if the actual driving time is greater than the planned time, the modulation factor will be adjusted in a more conservative direction to reduce the risk weight of the path; otherwise, it will be adjusted in a more aggressive direction to increase the efficiency weight of the path.

[0048] Preferably, in order to further improve the accuracy of dynamic modulation factor correction, more dimensional data can be introduced when recording the path execution time deviation, such as deviation data of different time periods, deviation data of different road sections, etc., so as to more comprehensively reflect the changes in the traffic environment. In addition, for the calculation of the standard deviation of the deviation distribution, a sliding window method can be used, and only the deviation data in the most recent period of time can be considered to improve the real-time performance of the system. When correcting the dynamic modulation factor, a dynamic adjustment mechanism of the learning rate coefficient can be introduced to dynamically adjust the learning rate according to the size of the deviation, such as increasing the learning rate when the deviation is large to correct it quickly, and reducing the learning rate when the deviation is small to avoid over-correction. In addition, the analysis of historical data can also be introduced, and the deviation data can be modeled through a machine learning algorithm to more accurately predict and correct the dynamic modulation factor. Through these refinement and optimization measures, the adaptive ability and reliability of the path planning system can be further improved.

[0049] In some embodiments, the medical resource matching parameter The calculation includes: S81. Construct a disease-resource mapping matrix ,in represents disease i requiring resource j, It means that disease i does not require resource j; S82. Based on patient vital signs parameters Generate symptom vector , Indicates the severity of symptom k; S83. Based on matrix D and vector Generate matching parameters: ,in, is the capacity of resource j, is the usage of resource j.

[0050] It should be noted that the present invention realizes the accurate calculation of medical resource matching parameters by constructing a disease-resource mapping matrix and generating a symptom vector based on the patient's vital signs parameters. This process is an important link in resource matching in emergency route planning, and aims to quickly match the most suitable medical resources according to the patient's specific condition. Among them, the disease-resource mapping matrix is ​​used to clarify the medical resources required for different diseases, while the symptom vector reflects the severity of the patient's condition and specific needs. Through the combination of the two, the system can dynamically evaluate the matching degree of medical resources, thereby providing an important basis for emergency vehicles to select target medical institutions.

[0051] Specifically, the disease-resource mapping matrix is ​​a two-dimensional matrix whose rows represent disease types and columns represent medical resource types. The elements in the matrix indicate whether a certain disease requires a certain resource, with a value of 1 indicating need and a value of 0 indicating no need. This matrix can be pre-constructed based on medical knowledge and clinical experience and updated based on actual conditions. The symptom vector is generated based on the patient's vital signs, including key indicators such as heart rate, blood pressure, and respiratory rate. After normalization, the value of each indicator forms a vector that reflects the severity of the patient's condition. The calculation of the medical resource matching parameter combines the symptom vector with the disease-resource mapping matrix, taking into account the resource capacity and occupancy of the medical institution, and finally obtains a quantitative value to evaluate whether the medical institution can meet the needs of the patient.

[0052] Preferably, in order to improve the accuracy and practicality of the disease-resource mapping matrix, the opinions of multidisciplinary experts can be introduced, and the clinical guidelines and actual case data can be combined for dynamic updates. For example, for some complex diseases, a detailed description of the number of specialists and equipment resources can be added. When generating symptom vectors, the vital sign parameters can be weighted according to the characteristics of different diseases. For example, for cardiovascular diseases, the weights of heart rate and blood pressure can be appropriately increased. In addition, the time factor can be introduced to dynamically adjust the calculation method of the resource matching parameters according to the urgency of the patient's condition. For example, for patients in critical condition, the system can give priority to medical institutions that are close and have sufficient resources. Through these refinement and optimization measures, the accuracy and practicality of the medical resource matching parameters can be further improved, providing more reliable decision support for emergency path planning.

[0053] In some embodiments, the security weight The calculation includes: S91. Obtain the historical accident frequency of road section k , is the number of accidents during period t; S92. Calculate road condition coefficient , Score lane width, Score the lighting; S93. Generate dynamic security weight: , Set speed limit for the road section.

[0054] It should be noted that the present invention provides an important safety assessment basis for emergency route planning by calculating the safety weight of the road section. The calculation of the safety weight comprehensively considers factors such as the historical accident frequency of the road section, the road condition coefficient, and the speed limit, and can dynamically reflect the safety of the road section. Among them, the historical accident frequency is used to assess the accident risk level of the road section, the road condition coefficient reflects the impact of the physical characteristics of the road on safety, and the speed limit is used as a reference factor to adjust the dynamic changes of the safety weight. In this way, the system can give priority to road sections with higher safety in route planning, thereby reducing the risk of accidents during the emergency treatment process.

[0055] Specifically, the historical accident frequency of a road section is obtained by counting the number of accidents in the road section within a certain period of time, which reflects the accident risk level of the road section under normal traffic conditions. The road condition coefficient is composed of the lane width score and the lighting score, which respectively reflect the road's traffic capacity and the safety of night driving. The lane width score is evaluated based on the actual width of the lane. The wider the width, the higher the score; the lighting score is evaluated based on the degree of perfection of the road lighting facilities. The better the lighting conditions, the higher the score. In the calculation formula of the safety weight, the weight of the historical accident frequency is low, while the weight of the road condition coefficient is high, which reflects the importance of the physical conditions of the road in safety assessment. In addition, the speed limit is used as a regulating factor to fine-tune the safety weight through an exponential decay function, further reflecting the impact of speed on safety.

[0056] Preferably, when calculating the safety weight, more parameters related to road safety can be introduced, such as road slope, curve radius, etc. These parameters can more comprehensively reflect the impact of the physical characteristics of the road on safety. For example, for sections with larger slopes, their safety weights can be appropriately reduced; for sections with smaller curve radius, the weights can also be adjusted to reflect their higher accident risks. In addition, the statistics of historical accident frequencies can be further refined into different time periods (such as daytime and nighttime) and different seasons to more accurately reflect the safety changes of the sections. At the same time, machine learning algorithms can be introduced to automatically adjust the weights of various parameters through the analysis of historical accident data, thereby achieving more accurate safety weight calculations. Through these refinement and optimization measures, the accuracy and practicality of the road section safety weights can be further improved, providing a more reliable safety assessment basis for emergency path planning.

[0057] In some embodiments, the specialty matching degree The calculation includes: S101. Extract the main symptoms of patients ; S102. Query the mapping table to obtain , Specialized configuration for medical institutions; S103. Calculate dynamic matching degree: , To delay time, is the disease half-life parameter.

[0058] It should be noted that the present invention achieves an accurate match between the specialist capabilities of medical institutions and the patient's condition requirements by calculating the specialist matching degree. The calculation of the specialist matching degree is based on factors such as the patient's main symptoms, the specialist configuration of the medical institution, and the delay time, and can dynamically reflect the medical institution's reception capacity under specific conditions. Among them, the patient's main symptoms are determined by analyzing the patient's vital signs and medical history, the specialist configuration of the medical institution reflects the hospital's medical resources and professional capabilities in specific fields, and the delay time takes into account the impact of the time it takes for the emergency vehicle to arrive at the hospital on the patient's condition. In this way, the system can give priority to medical institutions with high specialist matching degrees in path planning, thereby improving the success rate of first aid.

[0059] Specifically, the main symptom of the patient refers to the most prominent symptom of the patient's current condition, such as chest pain, dyspnea, etc. These symptoms are determined by the initial diagnosis of the emergency personnel or the patient's self-report. The specialty configuration of a medical institution refers to the hospital's professional settings in a specific disease field, such as cardiovascular department, neurosurgery, etc. This information can be obtained through the hospital's public data or docking with the hospital's information system. The calculation of specialty matching first determines the basic matching by querying the mapping table, which is pre-set according to the hospital's specialty configuration and common disease types. Subsequently, the basic matching is dynamically adjusted in combination with the delay time and the disease half-life parameter, where the delay time reflects the estimated time for the emergency vehicle to arrive at the hospital, and the disease half-life parameter indicates the rate at which the patient's condition deteriorates over time. In this way, the specialty matching can comprehensively consider the urgency of the patient's condition and the receiving capacity of the medical institution.

[0060] Preferably, in order to further improve the accuracy of specialist matching, more parameters related to the patient's condition, such as the patient's age, past medical history, etc., can be introduced in the calculation process. These parameters can more comprehensively reflect the complexity of the patient's condition. At the same time, the setting of the disease half-life parameter can be dynamically adjusted in combination with clinical data. For example, for certain acute diseases, the half-life parameter can be set shorter to reflect the rapid deterioration of the condition. In addition, an expert system or machine learning algorithm can be introduced to automatically optimize the calculation model of specialist matching by learning from a large number of historical emergency cases. For example, the system can automatically adjust the basic matching weights in the mapping table according to the success rate of specialist matching for different diseases. Through these refinement and optimization measures, the accuracy and practicality of specialist matching can be further improved, providing more reliable decision support for emergency path planning.

[0061] The above-mentioned embodiments of the present invention have the following beneficial effects: the dynamic route planning method of the emergency system of the present invention can comprehensively consider multi-dimensional factors such as patient vital signs, real-time traffic conditions, accident risks, and resource status of medical institutions, and dynamically optimize and prioritize the path through improved dynamic modulation factors and three-dimensional demodulation matrices. This method can dynamically adjust the path planning according to real-time data to ensure that the emergency vehicle selects the optimal path in a complex urban traffic environment, thereby effectively shortening the emergency response time and improving the efficiency of emergency. At the same time, through a multi-level logical judgment mechanism, the path score is calibrated in real time and the dynamic modulation factor is corrected to further improve the adaptability and accuracy of the path planning. Combined with the status data of medical institutions and the needs of patients' conditions, this method can achieve accurate matching of resources, ensure that patients are sent to the most suitable medical institutions in the shortest time, and significantly improve the success rate of emergency treatment and the quality of patient treatment. In addition, the present invention can further optimize the path selection and resource allocation through the calculation of dynamic safety weights and specialist matching, and improve the overall performance and reliability of the emergency system.

[0062] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0063] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A dynamic route planning method for an emergency system, characterized in that: The following steps are involved: S1. Obtaining a first aid request data set, the data set includes patient vital sign parameters , patient location coordinates , emergency vehicle real-time location coordinates ; S2. Real-time collection of dynamic traffic data sets in the target area, including the speed of each road section , traffic control area coordinate set , Real-time accident point coordinate queue The severity of the accident ; S3. Obtain medical institution status dataset, including emergency department admission capacity , Emergency channel occupancy status , Medical resource matching parameters ; S4. Based on the emergency request data set, dynamic traffic data set and medical institution status data set, an improved dynamic modulation factor is used Calculate the initial path set ; S5. Using the 3D demodulation matrix For the initial path set Prioritize the three-dimensional demodulation matrix The dimensions include: path safety parameters calculated based on real-time accident point data , based on the road segment speed Timeliness parameters , based on medical resource matching parameters and emergency department capacity Resource matching parameters ; S6. Execute multi-level logical judgment to generate the final navigation path ; Specifically include: S61. According to the fuel level of the emergency vehicle With threshold The comparison result triggers path recalculation; S62. Based on real-time accident point queue In Update the speed of the road section ; S63. Combined with the medical institution status data set and Calibrated Path Scoring ; S7. Execution time deviation according to path Corrected dynamic modulation factor .

2. The method according to claim 1, characterized in that The improved dynamic modulation factor calculation in step S4 includes: S41. Constructing space-time coupling function: ， in, is the traffic flow change rate calculated based on the dynamic traffic dataset, is the spatial complexity calculated based on the road topology, is the weather impact factor calculated based on meteorological data, , , is the normalized weight coefficient; S42. Calculate the dynamic weight of the road section: ， in, is the actual distance from node i to node j, is the medical resource impact coefficient defined based on the medical institution status dataset, The emergency channel is occupied; is the original road section speed; S43. Generate dynamic adjacency matrix ,in, is the accident point attenuation factor, For the The impact of each accident point is attenuated. is the time decay constant, For accident point The time of occurrence.

3. The method according to claim 2, characterized in that The three-dimensional demodulation matrix construction in step S5 includes: S51. Calculate path security dimension parameters: ,in, The road segment is calculated based on historical accident data Safety weight, is the aggregated impact of the accident point, For road section The associated accident point set; S52. Calculate the timeliness dimension parameters: ,in, is the total path length, Based on dynamic traffic dataset Calculate the weighted average speed, For road section length, is the velocity fluctuation; S53. Calculate resource matching dimension parameters: ， in, The specialist match is calculated based on the patient's symptoms. is the weight of symptom s, To meet resource requirements, is the capacity reference constant, is the emergency department admission capacity obtained from the medical institution status dataset, The total amount of medical resources required for the patient's condition. Match dimension parameters for resources.

4. The method according to claim 1, characterized in that Step S61 includes: S611. When Based on the real-time location of the emergency vehicle and gas station coordinates in the dynamic traffic dataset , generate a set of alternative paths: ； S612. According to the scoring model, the candidate path set Reorder; S613. Choose to satisfy and The path as the final navigation path ,in, The preset safety threshold.

5. The method according to claim 1, characterized in that Step S62 includes: S621. For each accident point , based on its severity level Calculate the influence radius ; S622. Update the road section speed, as shown in the following formula: ， in, is the shortest distance between road section k and accident point a, is the set of accident points affecting road section k; S623. When there is , delete the corresponding road segment and generate a new adjacency matrix .

6. The method according to claim 3, characterized in that: Step S63 includes: S631. Based on the medical institution status data set and , calculate the load factor ; S632. Dynamically adjust resource matching weights: ,in ; S633. Generate calibrated path score: .

7. The method according to claim 6, characterized in that Step S7 includes: S71. Record the execution time deviation of each path ; S72. Calculate the modulation factor correction: ,in, is the standard deviation of the deviation distribution; S73. Update dynamic modulation factor: , is the preset time base constant.

8. The method according to claim 7, characterized in that The medical resource matching parameter The calculation includes: S81. Construct a disease-resource mapping matrix ; S82. Based on patient vital signs parameters Generate symptom vector , Indicates the severity of symptom k; S83. Based on matrix D and vector Generate matching parameters: ， in, is the capacity of resource j, is the occupancy of resource j, and m is the total number of medical categories.

9. The method according to claim 8, characterized in that The safety weight The calculation includes: S91. Obtain the historical accident frequency of road section k ; S92. Calculate road condition coefficient ; S93. Based on historical accident frequency and road condition coefficient Generate a dynamic security weight as shown in the following formula: ， in, Set speed limit for the road section.

10. The method according to claim 9, characterized in that The degree of specialty matching The calculation is shown in the following formula: ， in, To delay time, is the disease half-life parameter, It is the matching degree of basic major.

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