Pre-hospital emergency rescue command and dispatch optimization method and system

Abstract

The invention discloses a pre-hospital emergency rescue command and dispatch optimization method and system, and relates to the technical field of intelligent medical first aid, and the method comprises the steps: collecting the position coordinates of a patient and a first aid resource state, carrying out the medical resource matching through combining with the injury condition level, and constructing a three-dimensional resource matrix; converting the three-dimensional resource matrix into discrete decision topology, and performing multi-state parallel evolution through a quantum annealing processor to generate an optimal resource allocation scheme; based on the optimal resource allocation scheme, path optimization is carried out in combination with real-time road conditions, sudden obstacles and environment changes, and a driving route optimization instruction is generated; and in the process of executing the driving route optimization instruction, monitoring the vehicle driving state deviation and the multi-vehicle collaborative collision risk in real time, triggering a multi-dimensional perception collaborative optimization mechanism, and generating a dynamic driving route. According to the method, the global optimal solution of the high-dimensional resource combination is realized through quantization mapping from the three-dimensional resource matrix to the discrete decision topology in combination with the polymorphic parallel evolution and energy level transition of the superconducting quantum bit group.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent medical first aid, in particular to a pre-hospital emergency rescue command and dispatch optimization method and system. BACKGROUND

[0002] The pre-hospital emergency rescue dispatch technology has evolved from artificial experience-based decision-making to intelligent system support. The current mainstream technology integrates patient location information, hospital resource status and ambulance real-time location using a big data analysis platform, uses heuristic algorithms for resource allocation optimization, and combines real-time traffic flow prediction models to plan driving routes. The emergency data interaction format is standardized, the vehicle-road cooperation technology realizes partial linkage between ambulances and traffic signal systems, and the reinforcement learning model has achieved preliminary application in dynamic path adjustment.

[0003] Three-dimensional combination optimization problems (medical resource matching, geographical accessibility and timeliness) significantly delay decision-making due to computational complexity, and traditional solving methods produce super-polynomial level time overhead; the decoupling design of resource allocation and path execution modules causes coordination to break down, and when the ambulance encounters sudden environmental changes, the dispatch process needs to be restarted, causing response efficiency to decay and multi-vehicle coordination risks to accumulate. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a pre-hospital emergency rescue command and dispatch optimization method to solve the problems of dispatch delay and solution executability in dynamic scenarios.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a pre-hospital emergency rescue command and dispatch optimization method, which comprises, When the emergency center receives a distress signal, the injury grade is divided through multi-modal symptom analysis; Collect patient location coordinates and emergency resource status, and combine the injury grade to match medical resources, and construct a three-dimensional resource matrix; Convert the three-dimensional resource matrix into a discrete decision topology, and perform multi-state parallel evolution through a quantum annealing processor to generate an optimal resource allocation scheme; Based on the optimal resource allocation scheme, the path is optimized in combination with real-time traffic, sudden obstacles and environmental changes to generate driving route optimization instructions; During the execution of the driving route optimization instructions, the vehicle driving state deviation and multi-vehicle collision risk are monitored in real time, the multi-dimensional perception and coordination optimization mechanism is triggered, and the dynamic driving route is generated.

[0007] As a preferred scheme of the pre-hospital emergency rescue command and dispatch optimization method, wherein: when the emergency center receives the distress signal, the injury grade is divided through multi-modal symptom analysis, and the specific steps are as follows, When the emergency center receives the distress signal, start voice recording, guide the caller to describe the key symptom characteristics of the patient, and ask about the age and medical history, and generate a complaint record; Based on the complaint record, combined with the trauma score standard, the patient's consciousness grade and respiratory status are determined, and the physiological compensation risk grade is generated; According to the physiological compensation risk grade, combined with the environmental information of the patient, the injury grade is divided.

[0008] As a preferred scheme of the pre-hospital emergency rescue command and dispatch optimization method, wherein: the patient position coordinates and emergency resource state are collected, and the specific steps are as follows, The patient's position coordinates are obtained by a cellular network triangulation algorithm, and the time efficiency and geographical connectivity to the patient's position coordinates are calculated; Collect the emergency resource state and extract the dynamic resource state report from the emergency resource state.

[0009] As a preferred scheme of the pre-hospital emergency rescue command and dispatch optimization method, wherein: and combined with the injury grade, the medical resource matching is performed, and a three-dimensional resource matrix is constructed, and the specific steps are as follows, According to the patient's injury grade, match with the dynamic resource state report to generate a resource configuration instruction set; The resource configuration instruction set is evaluated for medical resource adaptation, a medical matching degree is generated, and a three-dimensional resource matrix is generated by combining time efficiency and geographical connectivity.

[0010] As a preferred scheme of the pre-hospital emergency rescue command and dispatch optimization method, wherein: the three-dimensional resource matrix is converted into a discrete decision topology, and a quantum annealing processor is used for multi-state parallel evolution to generate an optimal resource configuration scheme, and the specific steps are as follows, The time efficiency in the three-dimensional resource matrix is mapped to a weight node, the geographical connectivity is converted into a constraint relationship edge, and the medical matching degree is deconstructed into an interaction coefficient to generate a discrete decision topology; According to the discrete decision topology, initialize the quantum bit group, and after energy level transition, continuously monitor the energy convergence state of the quantum bit group by the quantum annealing device to generate evolution result data packets; Based on the evolution result data packet, perform reverse mapping of the quantum bit group to the entity resource, and verify the medical resource matching compliance and road traffic feasibility to generate the optimal resource configuration scheme.

[0011] As a preferred scheme of the pre-hospital emergency rescue command and dispatch optimization method, wherein: based on the optimal resource configuration scheme, the real-time traffic index is calculated in combination with the real-time road conditions, and the obstacle influence layer is obtained according to the sudden obstacles; Based on the optimal resource configuration scheme, the real-time traffic index is calculated in combination with the real-time road conditions, and the obstacle influence layer is obtained according to the sudden obstacles; The real-time traffic index and the obstacle influence layer are fused, and the optimized path trajectory coordinate set is generated by superimposing the environmental changes; The optimized path trajectory coordinate set is converted into the driving route optimization instruction.

[0012] As a preferred scheme of the pre-hospital emergency rescue command and dispatch optimization method, wherein: during the execution of the driving route optimization instruction, the vehicle driving state deviation and the multi-vehicle collision risk are monitored in real time, the multi-dimensional perception collaborative optimization mechanism is triggered, and the dynamic driving route is generated, and the specific steps are as follows, The driving route optimization instruction is executed, the vehicle position offset and the direction deviation angle are obtained, and the vehicle driving state deviation data set is generated; Based on the vehicle driving state deviation data set of the multiple ambulances, the risk quantification algorithm is used to obtain the multi-vehicle collision risk index, and the multi-vehicle collision risk warning instruction is generated; According to the multi-vehicle collision risk warning instruction, the path re-planning strategy is performed in real time, and the dynamic driving route is generated.

[0013] In a second aspect, the present application provides a pre-hospital emergency rescue command and dispatch optimization system, comprising, The injury grade division module divides the injury grade through multi-modal symptom analysis when the emergency center receives the distress signal; The medical resource matching module collects the patient position coordinates and the emergency resource state, and matches the medical resources in combination with the injury grade to construct a three-dimensional resource matrix; The multi-state parallel evolution module converts the three-dimensional resource matrix into a discrete decision topology, and performs multi-state parallel evolution through a quantum annealing processor to generate an optimal resource configuration scheme; The driving route optimization module optimizes the path based on the optimal resource configuration scheme in combination with the real-time road conditions, sudden obstacles and environmental changes to generate driving route optimization instructions; The route real-time adjustment module monitors the vehicle driving state deviation and the multi-vehicle collision risk in real time during the execution of the driving route optimization instruction, triggers the multi-dimensional perception collaborative optimization mechanism, and generates a dynamic driving route.

[0014] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the pre-hospital emergency rescue command and dispatch optimization method according to the first aspect of the present application.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the pre-hospital emergency rescue command and dispatch optimization method according to the first aspect of the present application.

[0016] The present application has the following beneficial effects: through quantum mapping of a three-dimensional resource matrix to a discrete decision topology, combined with polymorphic parallel evolution and energy level transition of a superconducting quantum bit group, global optimal solution of high-dimensional resource combination is achieved; through dynamic perception coordination of vehicle driving state deviation and multi-vehicle collision risk, a risk quantification mechanism based on spatial trajectory envelope and time window overlap degree is established, triggering a hierarchical re-planning strategy, solving the process restart delay caused by the fragmentation of resource matching and path execution. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0018] Fig. 1 The flowchart of the pre-hospital emergency rescue command and dispatch optimization method.

[0019] Fig. 2 The schematic diagram of the pre-hospital emergency rescue command and dispatch optimization system.

[0020] Fig. 3 The flowchart of three-dimensional resource matrix generation and quantum optimization.

[0021] Fig. 4 The flowchart of path optimization and dynamic driving route generation. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0023] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0024] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic under at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides a pre-hospital emergency rescue command and dispatch optimization method, comprising the following steps: S1: When the emergency center receives a distress signal, the injury grade is divided through multi-modal symptom analysis; When the emergency center receives a distress signal, start voice recording, guide the caller to describe the key symptoms of the patient, and ask about the age and medical history, and generate a complaint record form; Further, the emergency center operator answers the distress call and starts the recording device, conducts voice guidance, such as please describe whether the patient can clearly answer now, whether there is active bleeding, and whether the breathing is abnormally rapid, synchronously collects key medical parameters, such as please inform the patient's specific age and whether he / she has a long-term medical history such as heart disease or diabetes, and the operator will obtain the consciousness state description, bleeding site information, breathing characteristics, age value, and medical history name, and enter the electronic form corresponding column to complete the complaint record form generation.

[0026] Based on the complaint record form, combined with the trauma score standard, the consciousness grade and respiratory state of the patient are determined, and the physiological compensation risk level is generated; Further, the emergency center medical staff extracts the Glasgow score estimation value field (such as eye opening reaction 2 points, language reaction 3 points, and motor reaction 4 points, total score 9 points) in the complaint record form, determines the consciousness grade according to the consciousness grading rules in the trauma score standard (such as 8-12 points corresponding to the third level of somnolence state), synchronously analyzes the respiratory abnormality mark field (such as respiratory rate 28 times / minute) in the complaint record form, matches the respiratory state classification rules of the trauma score standard (>24 times / minute defines respiratory failure), and outputs the standardized respiratory state; the consciousness grade result (third level) and the respiratory state (failure) are combined according to the composite mapping logic (such as the third level of consciousness combined with respiratory failure triggering the medium risk level) to generate the physiological compensation risk level (low risk / medium risk / high risk) result.

[0027] It should be pointed out that the trauma score standard construction process is to set the Glasgow coma index rule (eye opening response 1-4 points corresponds to eye opening ability, language response 1-5 points corresponds to language ability, motor response 1-6 points corresponds to motor ability, total score 3-15 points maps consciousness classification such as 8-12 points defines III level drowsy state), respiratory state classification rule (respiratory rate <10 times / min is abnormal, 10-24 times / min is normal, >24 times / min defines failure such as 28 times / min determines respiratory failure) and composite mapping logic (three levels of consciousness combined with respiratory failure to trigger medium risk level); The medical staff of the emergency center applies the trauma score standard to handle the complaint record sheet: extract the Glasgow score estimate field (such as 2 points for eye opening + 3 points for language + 4 points for movement = 9 points) to determine the three levels of consciousness classification, analyze the respiratory abnormality marker field (such as 28 times / min) to output the respiratory failure state, and input the composite mapping logic to generate the physiological compensation risk level (medium risk).

[0028] It should be pointed out that the Glasgow score estimate field is a quantitative indicator of consciousness state calculated quickly based on the patient's clinical symptoms, including eye opening response score, language response score and motor response score. The sum of the three scores constitutes the total Glasgow score, which is used to objectively quantify the degree of coma. This field replaces professional equipment detection in emergency scenarios to achieve rapid classification of consciousness state.

[0029] According to the physiological compensation risk level, combined with the environment information of the patient, the injury level is divided.

[0030] Further, the environment information of the patient is obtained, and the environment information includes location scene type, road traffic condition and meteorological parameter data. When the physiological compensation risk level is marked as high risk and the environment information contains a traffic accident scene, the injury level is forcibly marked as I level; if the physiological compensation risk level is high risk but the environment information shows a low risk scene, adjust to II level combined with meteorological parameter data; When the patient with medium risk level is superimposed with poor road traffic conditions, the injury level is upgraded to III level; the rest of the cases are mapped to IV level according to the risk level basic value, and finally form I / II / III / IV four-level injury level division conclusion.

[0031] S2: Collect patient location coordinates and emergency resource status, and match medical resources combined with injury level to construct a three-dimensional resource matrix; The patient's location coordinates are obtained by a cellular network triangulation algorithm, and the time efficiency and geographical connectivity to the patient's location coordinates are calculated; Furthermore, the communication service provider uses a cellular network triangulation algorithm to retrieve the signal strength values ​​of the three base stations closest to the patient and calculates the latitude and longitude coordinates based on the time difference of arrival principle; the emergency command center loads a road network structure with a radius of five kilometers centered on the latitude and longitude coordinates in real time, uses the Havesing formula to calculate the shortest travel time to the patient's location coordinates and corrects the road condition impact factor (obtained through an exponential decay function based on the real-time traffic congestion index), and outputs a time efficiency value in minutes; the geographic information platform extracts the vector data of passable roads within five kilometers of the coordinate point, analyzes the density of connecting nodes and real-time traffic capacity, and generates geographic connectivity.

[0032] It should be noted that geographic connectivity is a comprehensive indicator that quantifies the traffic capacity of the road network around the patient's location. It is generated by weighted fusion of the density of road intersections within a five-kilometer radius, the ratio of real-time average vehicle speed to road design speed, and the penetration loss coefficient of sudden obstacles, reflecting the road traffic efficiency for ambulances to reach the patient's location.

[0033] Time performance calculation formula: ; in, Indicates time efficiency. Indicates the straight-line distance from the patient to the ambulance. This indicates the legal speed limit on city roads. This represents the path nonlinearity compensation coefficient (value range: 1.2~1.5). Indicates the first Road length, Indicates the first Average vehicle speed measured on the section of road No. The attenuation factor of the segment path, This indicates the total number of path segments.

[0034] Geographic connectivity calculation formula: ; in, Indicates geographic connectivity. Indicates the number of road intersections. Indicates the area of ​​the region. Real-time average vehicle speed Indicates the maximum speed limit on the road. No. Disorder-related influencing factors This represents the weight of the contribution of road node density to traffic capacity (example: 0.4). The weight representing the degree of matching between the real-time vehicle speed and the design speed (example: 0.3). The weight representing the combined penetration capability of multiple obstacles (example: 0.3). This indicates the number of obstacle categories.

[0035] Collect the status of emergency medical resources and extract dynamic resource status reports from them; Furthermore, emergency center dispatchers retrieve real-time equipment status lists from ambulance onboard terminals, bed occupancy data from the emergency department database, and authorization and authentication information from the medical management platform to determine ambulance equipment availability, hospital admission load levels, and medical staff qualification matching status. Based on the determination results, a dynamic resource status report is generated, which includes three main fields: ambulance equipment availability list, hospital reception capacity level, and medical staff skills matrix.

[0036] Based on the patient's injury level, a set of resource configuration instructions is generated by matching it with the dynamic resource status report; Furthermore, based on the patient's injury level, emergency center dispatchers forcibly match commands for a intensive care ambulance, a qualified cardiologist, and the activation of a tertiary hospital's catheterization lab when the injury level is marked as Level I. Simultaneously, the ambulance equipment availability list, hospital reception capacity level, and medical skills matrix from the dynamic resource status report are loaded. Dispatchers perform a verification and matching operation. If the ambulance equipment availability list includes a ventilator standby device identifier, the hospital reception capacity level indicates the catheterization lab is available, and the medical skills matrix contains a valid cardiologist qualification, then a resource configuration instruction set is generated: personnel dispatch instructions, equipment activation instructions, and hospital preset instructions. If the dynamic resource status report shows resource deficiencies, a secondary priority rule is triggered, generating an instruction set with resource adaptation markers, and finally outputting a resource configuration instruction set including a timestamp.

[0037] It should be noted that the resource configuration instruction set is the core operation instruction entity for emergency resource dispatch, including personnel dispatch instructions, equipment activation instructions, and hospital instructions. By verifying the equipment availability, hospital reception capacity, and medical staff qualification matrix in the dynamic resource status report, when a resource is missing, a degradation rule is triggered and an adaptation mark is added, ultimately forming a standardized resource configuration operation instruction set with timestamps, driving a collaborative response among ambulances, personnel, and hospitals.

[0038] The system performs a medical resource adaptation assessment on the set of resource allocation instructions, generates a medical matching degree, and combines time efficiency and geographical connectivity to generate a three-dimensional resource matrix.

[0039] More specifically, the dispatcher executes the medical resource adaptation evaluation according to the personnel scheduling instruction, the equipment dispatching instruction and the hospital instruction contained in the resource configuration instruction set: obtains the percentage value of the actual matching resource to the standard demand, synchronously checks the completeness of the equipment dispatching instruction and the execution condition of the hospital instruction, and weightedly averages to generate the medical matching degree in the 0-1 scale; fills the medical matching degree, the time efficiency value and the geographical connection degree coefficient according to the three-dimensional resource matrix structure specification: writes the time efficiency value on the X-axis, embeds the geographical connection degree coefficient on the Y-axis, and loads the medical matching degree value on the Z-axis, to generate the three-dimensional resource matrix.

[0040] It should be noted that the medical matching degree is a comprehensive index for quantifying the degree of coincidence between the medical resource configuration scheme and the clinical demand, and the support capability of the resource configuration for the injury treatment is reflected by weightedly calculating the personnel quality coincidence rate, the equipment available completeness and the hospital reception condition matching value.

[0041] It should be noted that the three-dimensional resource matrix is a spatial data entity composed of the medical matching degree, the time efficiency and the geographical connection degree, wherein the medical matching degree quantifies the satisfaction degree of the resource configuration to the clinical demand, the time efficiency reflects the path passing efficiency in minutes, and the geographical connection degree represents the road network passing capacity, and the three are fused to form a quantitative evaluation framework of the emergency decision-making, and provide a structured input for quantum optimization.

[0042] S3: convert the three-dimensional resource matrix into a discrete decision topology, and perform multi-state parallel evolution through a quantum annealing processor to generate an optimal resource configuration scheme; The time efficiency in the three-dimensional resource matrix is mapped into a weight node, the geographical connection degree is converted into a constraint relationship edge, and the medical matching degree is decomposed into an interaction coefficient to generate a discrete decision topology; More specifically, the quantum operator extracts the time efficiency value of the X-axis in the three-dimensional resource matrix, applies a linear dimension conversion rule to map it into a weight node; inputs the geographical connection degree coefficient of the Y-axis into a constraint generation function to output a constraint relationship edge identifier; analyzes the medical matching degree value of the Z-axis, and directly takes it as an interaction coefficient after retaining three decimal precision; according to the discrete decision topology construction rule: associates the weight node as a topology vertex, converts the constraint relationship edge identifier into a connection strength parameter between vertices, and injects the interaction coefficient into the vertex association relationship, to generate a discrete decision topology entity containing vertex attribute values, connection strength parameters and association coefficients.

[0043] It should be noted that the discrete decision topology is a mathematical entity that converts the three-dimensional resource matrix parameters into a graph theory structure, and its vertices are composed of the weight nodes mapped from the time efficiency, the edges are defined by the constraint relationship generated from the geographical connection degree, and the connection strength between the vertices is marked by the interaction coefficient converted from the medical matching degree, to form a discretized problem framework that can be solved by quantum optimization.

[0044] According to the discrete decision topology, a group of quantum bits is initialized, and the energy convergence state of the group of quantum bits is continuously monitored by a quantum annealer after the energy level transition, and an evolution result data packet is generated; Further, the quantum operator calls the weight node value, constraint relationship edge value and interaction coefficient in the discrete decision topology entity, maps each weight node to a quantum bit initialization parameter, loads the constraint relationship edge value as the coupling strength coefficient between quantum bits, and inputs the interaction coefficient into the quantum gate rotation matrix parameter; the energy level transition operation of the quantum circuit is executed: a specific power microwave pulse is applied to drive the energy level transition of the group of quantum bits in a 0.1 tesla magnetic field environment, and the quantum annealer continuously captures the spin state probability distribution of the group of quantum bits at a millisecond-level sampling frequency; when the stability variance of the energy convergence state is less than 0.001 for three consecutive times, it is determined that the convergence is completed, the state of the group of quantum bits is frozen, and the spin direction of each quantum bit is read to output the binary coded evolution result data packet.

[0045] It should be noted that the energy level transition method maps the weight node value of the discrete decision topology to the quantum bit bias field strength, converts the constraint relationship edge value to the coupling coefficient between quantum bits, and inputs the interaction coefficient to the quantum gate rotation angle. In a static magnetic field environment, a microwave pulse is applied to drive the group of quantum bits to evolve from the initial superposition state to the target ground state adiabatically. The quantum tunneling effect is used to cross the energy barrier, and the quantum annealer samples the spin state probability distribution at a millisecond-level frequency and calculates the energy variance. When the variance of three consecutive samplings is below the convergence limit, the quantum state is frozen.

[0046] It should be noted that the quantum annealer is a special computing device for tunneling effect and adiabatic evolution principle. By mapping the weight nodes of the discrete decision topology to the quantum bit bias field, loading the constraint relationship edge value as the coupling strength between quantum bits, and inputting the interaction coefficient to the quantum gate rotation angle, a microwave pulse is applied to drive the energy level transition of the group of quantum bits in a 0.1 tesla superconducting magnetic field environment. The probability distribution of the quantum spin state is continuously monitored until the energy converges, and the millisecond-level global optimal solution search for the emergency resource combination optimization problem is realized.

[0047] Based on the evolution result data packet, the reverse mapping of the group of quantum bits to the entity resources is performed, and the medical resource matching compliance and the road traffic feasibility are verified, and an optimal resource allocation scheme is generated.

[0048] Further, the evolution result data packet binary code is parsed, each bit is associated with an entity resource according to a preset mapping table of a quantum bit group-resource object, medical resource matching compliance verification is performed, the ambulance equipment availability list, the hospital authority qualification database and the hospital bed state are checked, the road traffic feasibility verification calls the geographic information platform data, and when the double verification passes, an optimal resource configuration scheme is generated, including an approved instruction set: the device shipment instruction is bound to the ambulance respirator, the personnel scheduling instruction is replaced by a nurse, and the hospital instruction is revised to a hospital orthopedic bed preparation; if the verification conflicts, the discrete decision topology parameters are rolled back and restarted.

[0049] S4: Based on the optimal resource configuration scheme, the path is optimized in combination with the real-time traffic, the sudden obstacles and the environmental changes to generate driving route optimization instructions; Based on the optimal resource configuration scheme, the real-time traffic index is calculated in combination with the real-time traffic, and the obstacle influence layer is obtained according to the sudden obstacles; Further, based on the ambulance travel path coordinate sequence indicated in the optimal resource configuration scheme, the road flow rate monitoring data of the path coverage area is collected, the main road and auxiliary road traffic speeds are weighted and fused to generate a real-time traffic index, the sudden obstacle information center point position coordinates are synchronously extracted, the obstacle influence layer containing three-dimensional vector space feature description, influence range division, intensity parameter identification and traffic direction constraint label is created, and the layer structure is packaged into a standardized geographic space data format entity.

[0050] It should be noted that the obstacle influence layer is a geographic information entity describing the spatial influence characteristics of the sudden obstacle, containing the three-dimensional coordinates of the obstacle center point, the influence range geometric boundary, the intensity quantitative parameter and the traffic direction constraint condition, and the spatial attributes and traffic restriction rules are packaged in a standardized geographic data format.

[0051] The real-time traffic index calculation formula is: ; Wherein, represents the real-time traffic index, represents the average speed of the main road, represents the average speed of the auxiliary road, represents the road network topology correction factor, represents the main road weight (0.6), represents the auxiliary road weight (0.4), represents the number of obstacle categories, represents the th obstacle influence factor.

[0052] The real-time traffic index is fused with the obstacle influence layer, and the optimized path trajectory coordinate set is generated by superimposing the environmental changes; Further, load real-time traffic index and obstacle influence layer, perform spatial overlay analysis to identify low-capacity road segments, generate detour path buffer zone; simultaneously access real-time environmental change parameters released by meteorological agencies, load meteorological parameter dynamic correction on the detour path buffer zone trajectory; reconstruct trajectory point sequence according to road network topology rules, output optimized path trajectory coordinate set containing discrete longitude and latitude coordinate values.

[0053] Convert the optimized path trajectory coordinate set into driving route optimization instructions.

[0054] More specifically, receive the optimized path trajectory coordinate set and perform a three-step conversion operation: first, perform real-time matrix conversion from geodetic coordinate system to vehicle-mounted coordinate system, then apply a travel compression algorithm to encode the difference values of consecutive trajectory points, and finally encapsulate them into binary instruction frames according to the vehicle-mounted control bus communication protocol; the conversion result, driving route optimization instructions, includes: instruction header identifier, steering angle parameter, throttle opening ratio, and heading correction increment, forming a vehicle-grade control instruction entity.

[0055] S5: During the execution of the driving route optimization instructions, real-time monitoring of vehicle driving state deviation and multi-vehicle collision risk triggers a multi-dimensional perception and collaborative optimization mechanism to generate a dynamic driving route; Execute the driving route optimization instructions to obtain the vehicle position offset and direction deviation angle, and generate the vehicle driving state deviation data set; More specifically, the ambulance driver executes the driving route optimization instructions to control the vehicle, and real-time collects the current longitude and latitude coordinates of the vehicle, compares the coordinate sequence and speed control parameters contained in the driving route optimization instructions to obtain the planar position offset; simultaneously reads the heading angle data output by the vehicle-mounted gyroscope and calculates the direction deviation angle with the path tangent direction angle; after aligning the positioning and attitude sensor data with the time stamp, a structured vehicle driving state deviation data set is generated, containing fields: vehicle number, time stamp string, position offset value, and direction deviation angle value.

[0056] Based on the vehicle driving state deviation data set of multiple ambulances, a risk quantification algorithm is used to obtain a multi-vehicle collision risk index, and a multi-vehicle collision risk warning instruction is generated; Further, the emergency command center receives the vehicle driving state deviation data set uploaded by multiple ambulances, calls the risk quantification algorithm to perform a two-dimensional evaluation: spatial dimension obtains the intersection area ratio of vehicle trajectory envelopes, and time dimension analyzes the absolute value of the time difference to the conflict point; multiply the spatial overlap ratio and the time difference reciprocal to generate a multi-vehicle collision risk index; when the index exceeds the warning value, generate a multi-vehicle collision risk warning instruction, containing conflict vehicle number group, collision point longitude and latitude, risk level, and time window.

[0057] It should be noted that the risk quantification algorithm evaluates the emergency vehicle cooperative collision risk through a two-dimensional coupling mechanism: the spatial dimension constructs a dynamic trajectory envelope area and calculates the overlap ratio, and the time dimension predicts the conflict point position and the degree of overlap of the time window; the spatial overlap ratio and the time overlap degree are fused according to the weight to generate a multi-vehicle cooperative collision risk index, when the index exceeds the warning limit (set by statistical analysis of historical emergency vehicle accident data, configured according to vehicle type, road level and environmental conditions), output warning instructions containing conflict vehicle number group, collision point coordinates, risk level classification and time window parameters, realize real-time quantitative warning and cooperative collision avoidance decision support of emergency vehicle driving safety.

[0058] According to the multi-vehicle cooperative collision risk warning instruction, the path re-planning strategy is carried out in real time, and a dynamic driving route is generated.

[0059] More specifically, the emergency dispatch center analyzes the conflict vehicle group identifier, collision point coordinates, risk level and time window contained in the multi-vehicle cooperative collision risk warning instruction, and triggers a hierarchical path re-planning strategy according to the risk level: the highest priority strategy is executed for the high-risk conflict vehicle group — maintain the original path but activate the traffic signal priority control, the unmanned aerial vehicle cooperative guidance strategy is started for the secondary risk vehicle group, and the alternative path speed reduction strategy is executed for the basic risk vehicle group; based on the strategy output, the trajectory coordinate sequence is reacquired, and a dynamic driving route entity containing discrete latitude and longitude coordinate points and speed control parameters is generated.

[0060] The embodiment also provides a pre-hospital emergency rescue command and dispatch optimization system, comprising: The injury level division module divides the injury level through multi-modal symptom analysis when the emergency center receives the distress signal; The medical resource matching module collects patient position coordinates and emergency resource status, and matches medical resources combined with the injury level to construct a three-dimensional resource matrix; The multi-state parallel evolution module converts the three-dimensional resource matrix into a discrete decision topology, and performs multi-state parallel evolution through a quantum annealing processor to generate an optimal resource allocation scheme; The driving route optimization module generates driving route optimization instructions based on the optimal resource allocation scheme, combined with real-time road conditions, sudden obstacles and environmental changes; The route real-time adjustment module monitors the vehicle driving state deviation and multi-vehicle cooperative collision risk in real time during the execution of the driving route optimization instructions, triggers a multi-dimensional perception cooperative optimization mechanism, and generates a dynamic driving route.

[0061] The embodiment also provides a computer device suitable for the pre-hospital emergency rescue command and dispatch optimization method, which comprises a memory and a processor.

[0062] The computer device can be a terminal, which comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0063] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the pre-hospital emergency rescue command and dispatch optimization method. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0064] To sum up, the application realizes the global optimal solution of high-dimensional resource combination by the quantum mapping of three-dimensional resource matrix to discrete decision topology, combined with the polymorphic parallel evolution and energy level transition of superconducting quantum bit group; and establishes a risk quantification mechanism based on the coincidence degree of space trajectory envelope and time window, triggers a hierarchical re-planning strategy, and solves the process restart delay caused by the fragmentation of resource matching and path execution.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing pre-hospital emergency care command dispatching, characterized in that: The application relates to an emergency medical service optimization method and device. When the emergency center receives the distress signal, the injury grade is divided through multi-modal symptom analysis; The patient position coordinates and emergency resource state are collected, and medical resource matching is performed in combination with the injury grade to construct a three-dimensional resource matrix; The three-dimensional resource matrix is converted into a discrete decision topology, and multi-state parallel evolution is performed through a quantum annealing processor to generate an optimal resource allocation scheme; Based on the optimal resource allocation scheme, path optimization is performed in combination with real-time road conditions, sudden obstacles and environmental changes to generate driving route optimization instructions; During the execution of the driving route optimization instructions, the vehicle driving state deviation and multi-vehicle collision risk are monitored in real time, a multi-dimensional perception collaborative optimization mechanism is triggered, and a dynamic driving route is generated.

2. The pre-hospital emergency care command dispatch optimization method of claim 1, wherein: When the emergency center receives the distress signal, the injury grade is divided through multi-modal symptom analysis, and the specific steps are as follows, When the emergency center receives the distress signal, the voice recording is started, the caller is guided to describe the key symptom characteristics of the patient, and the age and past medical history are inquired, and a complaint record is generated; Based on the complaint record, the consciousness grade and respiratory state of the patient are determined in combination with the trauma score standard, and a physiological compensation risk grade is generated; According to the physiological compensation risk grade, the environmental information of the patient is combined to divide the injury grade.

3. The pre-hospital emergency care command dispatch optimization method of claim 2, wherein: The specific steps of collecting the patient position coordinates and the emergency resource state are as follows, The patient position coordinates are obtained through a cellular network triangulation algorithm, the time efficiency and geographical connectivity to the patient position coordinates are calculated, and the dynamic resource state report is extracted from the emergency resource state. The specific steps of matching the medical resource in combination with the injury grade to construct the three-dimensional resource matrix are as follows, 4. The pre-hospital emergency care command dispatch optimization method of claim 3, wherein: According to the injury grade of the patient, the dynamic resource state report is matched to generate a resource allocation instruction set; The resource allocation instruction set is subjected to medical resource adaptation evaluation to generate a medical matching degree, and the three-dimensional resource matrix is generated in combination with the time efficiency and the geographical connectivity. The specific steps of converting the three-dimensional resource matrix into a discrete decision topology and performing multi-state parallel evolution through a quantum annealing processor to generate an optimal resource allocation scheme are as follows, 5. The pre-hospital emergency care command dispatch optimization method of claim 4, wherein: The time efficiency in the three-dimensional resource matrix is mapped into a weight node, the geographical connectivity is converted into a constraint relationship edge, and the medical matching degree is deconstructed into an interaction coefficient to generate a discrete decision topology; According to the discrete decision topology, a group of quantum bits is initialized, and after energy level transition, the energy convergence state of the group of quantum bits is continuously monitored through a quantum annealing device to generate evolution result data packets; Based on the evolution result data packets, reverse mapping of the group of quantum bits to entity resources is performed, and medical resource matching compliance and road traffic feasibility are verified to generate an optimal resource allocation scheme. The specific steps of generating driving route optimization instructions based on the optimal resource allocation scheme in combination with real-time road conditions, sudden obstacles and environmental changes are as follows, 6. The pre-hospital emergency care command dispatch optimization method of claim 5, wherein: Based on the optimal resource allocation scheme, real-time traffic indexes are calculated in combination with real-time road conditions, and obstacle influence layers are obtained according to sudden obstacles; The real-time traffic indexes and the obstacle influence layers are fused, and an optimized path trajectory coordinate set is generated by superimposing environmental changes. ​ Convert the optimized path trajectory coordinate set into driving route optimization instructions.

7. The pre-hospital emergency care command dispatch optimization method of claim 6, wherein: During the execution of the driving route optimization instructions, the vehicle driving state deviation and the multi-vehicle collision risk are monitored in real time, the multi-dimensional perception collaborative optimization mechanism is triggered, and a dynamic driving route is generated. The specific steps are as follows, Execute the driving route optimization instructions, obtain the vehicle position offset and the direction deviation angle, and generate the vehicle driving state deviation data set; Based on the vehicle driving state deviation data set of multiple ambulances, the risk quantification algorithm is used to obtain the multi-vehicle collision risk index, and the multi-vehicle collision risk warning instruction is generated; According to the multi-vehicle collision risk warning instruction, the path re-planning strategy is performed in real time, and the dynamic driving route is generated.

8. A pre-hospital emergency rescue command and dispatch optimization system based on the pre-hospital emergency rescue command and dispatch optimization method of any one of claims 1-7, characterized in that: It includes, The injury grade division module divides the injury grade through multi-modal symptom analysis when the emergency center receives the distress signal; The medical resource matching module collects the patient position coordinates and the emergency resource state, and matches the medical resources combined with the injury grade to construct a three-dimensional resource matrix; The multi-state parallel evolution module converts the three-dimensional resource matrix into a discrete decision topology, and performs multi-state parallel evolution through a quantum annealing processor to generate an optimal resource allocation scheme; The driving route optimization module optimizes the path based on the optimal resource allocation scheme, combines the real-time road conditions, sudden obstacles and environmental changes, and generates driving route optimization instructions; The route real-time adjustment module, during the execution of the driving route optimization instructions, monitors the vehicle driving state deviation and the multi-vehicle collision risk in real time, triggers the multi-dimensional perception collaborative optimization mechanism, and generates a dynamic driving route. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the pre-hospital emergency rescue command dispatching optimization method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the pre-hospital emergency rescue command dispatching optimization method of any one of claims 1-7.

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