Emergency response method, vehicle, and readable medium

Abstract

The emergency response method, vehicle and readable medium provided by the application relate to the technical field of intelligent traffic and smart medical treatment. The method comprises the following steps: in response to identifying an abnormal event of a target user, determining a disease level of the target user; matching an emergency required capability corresponding to the disease level from a mapping relationship between preset disease levels and emergency required capabilities; screening a candidate hospital set meeting the emergency required capability from a medical resource database; calculating a comprehensive score between each candidate hospital in the candidate hospital set and the target user; determining a target hospital from the candidate hospital set based on the comprehensive score of each candidate hospital, and sending disease information of the target user to the target hospital to trigger an emergency resource preparation process of the target hospital. The application can avoid medical resource mismatch, thereby triggering the emergency resource preparation process of the target hospital in advance, shortening the treatment response cycle and avoiding the problem of delayed best treatment opportunity.

Description

Technical Field

[0001] This application relates to the interdisciplinary fields of intelligent transportation and smart healthcare, and more specifically, to an emergency response method, vehicle, and readable medium. Background Technology

[0002] With the continuous improvement of public health awareness, the deep integration of intelligent vehicle systems and smart healthcare platforms has become a core development direction for improving the efficiency of emergency medical rescue. However, the current emergency medical system mainly relies on patients or passersby calling 120, the ambulance going to the scene, and then transporting the patient to the hospital. This model suffers from serious information lag, delaying the golden rescue time, and lacks a real-time vital sign sharing mechanism between vehicles and hospitals. This results in response delays, information isolation, and resource misallocation in the path from the occurrence of an incident to the patient receiving effective treatment, seriously affecting the success rate of emergency rescue and the utilization rate of medical resources.

[0003] To address the aforementioned issues, existing technologies have proposed several solutions. For example, some emergency medical systems automatically alert the emergency center and send the vehicle's location information to the vehicle when an accident is detected by the vehicle's onboard terminal, thereby shortening the response time. Other emergency medical systems use monitoring equipment on the vehicle to upload the patient's physiological data (such as electrocardiogram) to the hospital terminal for emergency physicians to view, so as to prepare medical resources in advance.

[0004] However, existing emergency medical systems often select target hospitals based on fixed rules or distance alone after being triggered, failing to dynamically and intelligently adapt to the patient's real-time condition, the hospital's specialty capabilities, and workload. This leads to misallocation of medical resources and delays in receiving optimal treatment. Summary of the Invention

[0005] This application provides an emergency response method, vehicle, and readable medium. The method determines the target hospital based on the severity of the illness, avoids misallocation of medical resources, and thus triggers the emergency resource preparation process of the target hospital in advance, shortens the treatment response cycle, and avoids the problem of delaying the best treatment time.

[0006] Firstly, an emergency response method is provided, comprising: in response to the identification of an abnormal event of a target user, determining the target user's condition level; matching the emergency response capabilities corresponding to the condition level from a preset mapping relationship between condition levels and emergency response capabilities; filtering a set of candidate hospitals that meet the emergency response capabilities from a medical resource database; calculating a comprehensive score between the candidate hospital and the target user for each candidate hospital in the candidate hospital set; the comprehensive score is used to characterize the expected emergency response cost of sending the target user to each candidate hospital; and based on the comprehensive score of each candidate hospital, determining the target hospital from the candidate hospital set and sending the target user's condition information to the target hospital to trigger the target hospital's emergency resource preparation process.

[0007] In the above technical solution, in response to the identification of abnormal events of the target user, the severity level of the target user's condition is determined to achieve a quantitative assessment of the patient's real-time condition. Based on the preset mapping relationship between the severity level and the emergency response capabilities required, the corresponding emergency response capabilities are matched to achieve a quantitative assessment of the emergency response capabilities. A set of candidate hospitals that meet the emergency response capabilities required is then selected from the medical resource database to complete the initial adaptation with the hospital's specialized treatment capabilities. A comprehensive score is calculated for each candidate hospital and its relationship with the target user. This comprehensive score is used to characterize the expected emergency response cost of sending the target user to the corresponding hospital, so as to achieve a dynamic fusion assessment of multiple dimensions such as real-time hospital load, route accessibility, and treatment suitability, thus avoiding the shortcomings of single-rule selection. Based on the comprehensive scores of each candidate hospital, the target hospital is selected from the candidate hospital set to effectively avoid medical resource mismatch. At the same time, the target user's condition information is sent to the target hospital to trigger the target hospital's emergency resource preparation process in advance, thereby shortening the treatment response cycle and avoiding the problem of delaying the best treatment opportunity.

[0008] Secondly, an emergency response device is provided, comprising: a first determining module for determining the severity level of a target user's condition in response to the identification of an abnormal event; a matching module for matching the required emergency response capabilities corresponding to the severity level from a preset mapping relationship between severity level and required emergency response capabilities; a filtering module for filtering a set of candidate hospitals that meet the required emergency response capabilities from a medical resource database; a calculation module for calculating a comprehensive score between the candidate hospital and the target user for each candidate hospital in the candidate hospital set; the comprehensive score is used to characterize the expected emergency response cost of sending the target user to each candidate hospital; and a second determining module for determining a target hospital from the candidate hospital set based on the comprehensive score of each candidate hospital, and sending the target user's condition information to the target hospital to trigger the target hospital's emergency resource preparation process.

[0009] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof.

[0010] Fourthly, a computer-readable storage medium is provided that stores a program or instructions that cause a computer to perform the methods described in the first aspect or any possible implementation thereof.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of this application more easily understood, specific embodiments of this application are given below. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of the structure of a vehicle provided in an embodiment of this application; Figure 2 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 1 ; Figure 3 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 2 ; Figure 4 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 3 ; Figure 5 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 4 ; Figure 6 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 5 ; Figure 7 This is a schematic diagram of the structure of an emergency response device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0014] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0015] While the collaborative development of intelligent vehicle systems and smart healthcare platforms offers direction for improving the efficiency of emergency medical rescue, traditional emergency rescue models rely on manual alarms and on-site transfers, which suffer from problems such as information lag and the inability to share vital sign data in real time. This can easily delay the golden rescue time, reducing the success rate of emergency rescue and the utilization rate of medical resources. Although existing emergency rescue systems can achieve basic optimizations such as automatic accident alarms and uploading of patient physiological data, shortening response time to some extent and assisting hospitals in preparing resources in advance, they still generally use fixed rules or only select treatment hospitals based on distance. They cannot combine the patient's real-time condition, the hospital's specialty capabilities, and the workload to carry out dynamic intelligent comprehensive adaptation, and the problems of medical resource mismatch and delays in the best treatment time still exist.

[0016] The emergency response method, system, and vehicle of this application will be described in detail below with reference to the accompanying drawings and through multiple embodiments.

[0017] Figure 1 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Figure 1 As shown, the vehicle 100 may include a processor 110 and a memory 120.

[0018] The memory 120 stores machine-executable instructions that can be executed by the processor 110. When the vehicle 100 is running, these machine-executable instructions are executed. The processor 110 communicates with the memory 120 via a bus. The processor 110 can execute these machine-executable instructions to implement emergency response methods.

[0019] The memory 120, processor 110, and various bus components are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 120 includes at least one software functional module, which is stored or embedded in the vehicle's operating system (OS) in the form of software or firmware, and includes an executable module. The processor 110 is used to execute the executable module stored in the memory 120, such as the software functional modules and computer programs included in emergency response methods.

[0020] The memory 120 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0021] The vehicle 100 can be selected according to the actual situation. Furthermore, the vehicle 100 is equipped with software capable of executing emergency response methods.

[0022] The emergency response method provided in this application embodiment can be executed by the processor in the vehicle 100. The emergency response method provided in this application embodiment will be explained further below. Figure 2 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 1 .like Figure 2 As shown, the method may include: S210. In response to the identification of an abnormal event of the target user, determine the severity of the target user's condition.

[0023] In one possible implementation, the emergency medical system pre-binds a unique identifier to the target user (such as a user account, device MAC address, biometric information, etc.) to ensure a unique correspondence between monitoring data and the target user, avoiding data confusion. Then, the emergency medical system collects relevant data from the target user in real time through various connected monitoring devices (such as wearable devices, environmental monitoring devices, user terminal devices, etc.), including at least: physiological data, behavioral data, and environmental data. Physiological data includes at least: heart rate, blood pressure, and blood oxygen saturation; behavioral data includes at least: activity trajectory, operation frequency, and sleep patterns; environmental data includes at least: ambient temperature and humidity, noise, and air quality. The data collection frequency can be flexibly adjusted according to a preset frequency, for example, 1 time / minute or 5 times / minute.

[0024] Secondly, the collected relevant data of the target user is transmitted in real time to the data caching module of the emergency medical system. After preprocessing such as deduplication and noise reduction, it is transmitted to the data processing module. This data processing module compares the preprocessed real-time relevant data with preset normal range thresholds and preset normal behavior patterns based on preset recognition algorithms (such as threshold comparison algorithms, machine learning recognition models, etc.). When the real-time relevant data exceeds the preset normal range threshold, or the behavior pattern deviates significantly from the preset normal pattern (such as a sudden increase in heart rate to 120 beats / minute, no effective activity trajectory for 12 consecutive hours, or blood pressure consistently higher than 140 / 90 mmHg, etc.), an abnormal event of the target user is identified, and the corresponding response mechanism is triggered.

[0025] Upon identifying an abnormal event, the emergency medical system combines real-time data of the event with the target user's historical data. Real-time data includes at least: the abnormal data value, duration, and environmental and behavioral background data at the time of the abnormality; historical health data includes at least: historical abnormal event records, basic health records, and past medical diagnosis information. A comprehensive matching process is performed according to a preset disease severity classification standard to determine the target user's current disease severity level. The preset disease severity classification standard is shown in Table 1 below. Different disease severity levels correspond to different weighting strategies, which are used to assign different weights to indicators such as estimated travel time, real-time hospital load index, emergency medical capacity deviation, and available bed resources during subsequent emergency dispatch, in order to achieve precise dispatch matching the severity of the illness.

[0026] Table 1 shows the preset disease severity classification criteria and corresponding weighting strategies.

[0027] Among them, W1 is the weight corresponding to the estimated travel time; W2 is the weight corresponding to the hospital's real-time load index; W3 is the weight corresponding to the emergency response capacity deviation item; and W4 is the weight corresponding to the number of available bed resources.

[0028] It should be noted that while determining the severity of the illness, the emergency medical system generates a severity level assessment report, records abnormal data, assessment criteria, matching process, and other information, and synchronizes the severity level and assessment report to relevant terminals (such as user terminals, medical staff terminals, and family member-linked terminals). If the severity level reaches the emergency severity level or above, an early warning mechanism is activated simultaneously (such as sending emergency notifications to medical staff and activating the on-site emergency call interface) to ensure that the severity level assessment is accurate, efficient, and fully traceable.

[0029] S220. From the preset mapping relationship between the severity of illness and the emergency response capabilities required, match the emergency response capabilities required for the severity of illness.

[0030] The pre-defined mapping relationship between disease severity levels and required emergency response capabilities is a set of pre-established emergency response capability standards corresponding to different disease severity levels within the emergency medical system. These standards include at least: emergency treatment qualifications, critical care capabilities, emergency equipment configuration, specialist medical team configuration, and emergency bed reserves. This mapping relationship is updated and maintained by a backend administrator authorized by the medical management department. For example, a modification plan may be submitted by an expert team, reviewed and approved, and then entered into the emergency medical system to ensure the scientific validity and timeliness of the mapping relationship.

[0031] In one possible implementation, the emergency medical system, based on the target user's current real-time condition level and the preset mapping relationship between condition levels and emergency medical capabilities, uses the determined condition level as a query condition to retrieve the corresponding emergency medical capability information in the preset mapping relationship between condition levels and emergency medical capabilities, so as to achieve accurate matching of "condition level - emergency medical capability".

[0032] It should be noted that a dual verification mechanism is implemented during the matching process. The first verification is a validity verification, which confirms that the retrieved emergency medical capability information completely corresponds to the current illness level, and that the emergency medical capability information is the latest version in the preset mapping relationship between illness levels and emergency medical capabilities. If no corresponding emergency medical capability information is retrieved (e.g., an illness level not preset), an error message is triggered and feedback is sent to emergency personnel and the backend administrator. In this case, the highest level of emergency medical capability is matched by default to avoid missing emergency needs. The second verification is a rationality verification. The emergency medical system will combine the current emergency scenario (e.g., pre-hospital emergency care, in-hospital emergency triage) and the real-time status of regional emergency resources (e.g., equipment configuration and personnel configuration of nearby emergency vehicles) to verify whether the matched emergency medical capability can be implemented. If there are insufficient resources to meet the requirements, the system will adjust to the optimal emergency medical capability configuration that can be implemented nearby, and simultaneously notify the backend dispatch center for resource coordination.

[0033] Once the matching and verification are complete, the emergency medical system will synchronize the final determined target emergency medical capabilities to the front-end emergency medical terminals (such as emergency personnel's mobile apps and emergency vehicle terminals) and the back-end dispatch center. This will clearly inform emergency personnel of the personnel, equipment, and procedures to be followed, and inform the dispatch center of the resources to be coordinated, ensuring that emergency medical work is carried out in an orderly manner according to the target emergency medical capabilities. At the same time, the emergency medical system will fully record the entire process of determining the target emergency medical capabilities (this entire process includes at least: the severity of the illness, the mapping relationship, the matching results, and the verification records), and store it in the emergency medical system database. This will facilitate subsequent emergency medical quality traceability, process optimization, and responsibility determination, ensuring that the entire operation process is traceable and verifiable.

[0034] S230. Select a set of candidate hospitals that meet the emergency care requirements from the medical resource database.

[0035] The medical resource database is used to store relevant information about various medical institutions, and comprehensively includes all dimensions of medical resource information such as emergency response capabilities, department setup, medical equipment, medical qualifications, number of beds, and treatment scope of various medical institutions.

[0036] In one possible implementation, the emergency medical system, based on the determined emergency medical capability requirements matched to the severity of the illness, activates its built-in filtering and matching logic and algorithm to compare and verify the compliance of all hospital information in the medical resource database. During the verification process, hospitals that do not meet the corresponding emergency medical capability requirements are automatically eliminated, as well as hospitals whose emergency medical capabilities far exceed the needs of the illness, which could easily lead to the idleness and waste of medical resources. Only hospitals whose emergency medical capabilities are suitable for the target user's illness severity are retained as candidates, ultimately forming a set of candidate hospitals that only contain the qualifications and capabilities to undertake emergency medical care for the corresponding illness. This filtering process can ensure that the hospitals subsequently dispatched have the basic conditions to undertake the treatment of the corresponding illness, ensuring the safety and effectiveness of emergency medical care.

[0037] S240. For each candidate hospital in the candidate hospital set, calculate the comprehensive score between the candidate hospital and the target user.

[0038] The comprehensive score characterizes the expected emergency medical costs of transporting the target user to each candidate hospital. These expected costs are a quantified sum of all emergency-related costs, risks, and losses that may occur during the entire process of safely transferring the target user from their current location to the corresponding candidate hospital. Specifically, it covers at least three dimensions: time cost, resource cost, and risk cost. The time cost primarily includes the real-time travel time of the ambulance from the target user's location to the hospital, the hospital's emergency team's response time, and the user's waiting time at the hospital. The resource cost primarily includes the cost of ambulance occupancy, the cost of emergency medical personnel, and the cost of emergency equipment and medications. The risk cost primarily includes the risk of the user's condition worsening during transport, the risk of inadequate hospital emergency capabilities leading to poor treatment outcomes, and the risk of transport delays due to traffic congestion or accidents.

[0039] In one possible approach, firstly, feature data for each hospital in the candidate hospital set is extracted. This feature data must correspond to the core dimensions of the expected emergency response cost, including at least: the straight-line distance between the hospital and the target user, real-time traffic travel time, hospital emergency response speed, number of emergency medical personnel, success rate of diagnosis and treatment of the corresponding illness, and completeness of emergency equipment. Simultaneously, key information about the target user is extracted, including at least: the user's current illness type, urgency level, current location, and real-time physical condition data, providing a user-side basis for scoring calculations. Secondly, the extracted hospital feature data and target user key information are standardized to eliminate dimensional differences between different indicators. For example, different types of indicators such as distance, time, and personnel configuration are uniformly mapped to a scoring range of 0-10 points, ensuring that each indicator can directly participate in subsequent calculations. Finally, for each candidate hospital, a weighted calculation is performed based on each hospital's feature data and corresponding preset weights to obtain a comprehensive score between each candidate hospital and the target user, ensuring that each candidate hospital has a unique quantitative comprehensive score.

[0040] It is important to clarify that each indicator in the comprehensive score corresponds one-to-one with a specific dimension of the expected emergency medical costs. For example, real-time traffic time corresponds to time costs, hospital emergency medical capabilities (including treatment success rate and equipment completeness) correspond to risk costs, and emergency medical resource allocation (including medical personnel, equipment, and medicines) corresponds to resource costs. Furthermore, the comprehensive score is positively correlated with the expected emergency medical costs: a higher comprehensive score indicates a higher expected emergency medical costs for transporting the user to the corresponding candidate hospital; a lower comprehensive score indicates a lower expected emergency medical costs. Based on this correlation, by comparing the comprehensive scores of each candidate hospital, the candidate hospital with the lowest expected emergency medical costs can be quickly selected, and this hospital can be prioritized for emergency transport, ensuring emergency medical efficiency and treatment effectiveness.

[0041] S250. Based on the comprehensive score of each candidate hospital, determine the target hospital from the candidate hospital set and send the target user's medical information to the target hospital to trigger the target hospital's emergency resource preparation process.

[0042] In one possible implementation, the emergency medical system uses a pre-set algorithm (such as a weighted summation sorting algorithm or an optimal matching algorithm) to rank and compare the comprehensive scores of each hospital in the candidate hospital set, and selects the hospital with the lowest comprehensive score that best meets the current emergency needs of the patient's condition, and identifies it as the target hospital in this emergency medical process.

[0043] After the emergency medical system identifies the target hospital, it transmits the assessed patient condition information to the target hospital's emergency management system or designated receiving terminal (such as the emergency department nurse station or the emergency manager's terminal) through a pre-set secure communication channel (e.g., a dedicated medical encrypted network, a dedicated data transmission interface, etc.). During transmission, the security, integrity, and real-time nature of the information must be ensured to prevent information leakage or transmission delays. Upon receiving the patient's condition information, the target hospital's emergency management system automatically matches the pre-set emergency resource preparation plan corresponding to that condition (e.g., for critical cases, activating the emergency green channel, preparing ventilators, defibrillators, and other critical equipment, and deploying emergency specialist medical teams on standby; for moderate cases, preparing specialist examination equipment and medical personnel). The emergency system automatically sends resource preparation instructions to relevant departments within the hospital, such as the emergency department, equipment management department, and medical personnel dispatch center, initiating the emergency resource preparation process. This ensures that the target hospital can prepare for emergency care in advance based on the received condition information, efficiently undertaking the patient's emergency care.

[0044] The emergency response method provided in this application responds to the identification of abnormal events of a target user, determines the target user's condition level, and achieves a quantitative assessment of the patient's real-time condition. Based on a preset mapping relationship between the condition level and the required emergency response capabilities, it matches the corresponding required emergency response capabilities to quantify the required capabilities. It then selects a set of candidate hospitals from a medical resource database that meet these required capabilities to achieve initial matching with the hospital's specialized treatment capabilities. For each candidate hospital, it calculates a comprehensive score between the candidate hospital and the target user. This comprehensive score characterizes the expected emergency response cost of sending the target user to the corresponding hospital, enabling a dynamic fusion assessment of multiple dimensions such as real-time hospital load, route accessibility, and treatment suitability, thus overcoming the shortcomings of single-rule selection. Based on the comprehensive scores of each candidate hospital, it selects the target hospital from the candidate hospital set, effectively avoiding medical resource mismatch. Simultaneously, it sends the target user's condition information to the target hospital, triggering the target hospital's emergency resource preparation process in advance, thereby shortening the treatment response cycle and avoiding delays in optimal treatment.

[0045] Figure 3A flowchart illustrating an emergency response method provided in this application embodiment. Figure 2 .like Figure 3 As shown, the above method filters a set of candidate hospitals that meet the emergency medical needs from a medical resource database, including: S310. Using the target user's current location as the center, select a preliminary set of candidate hospitals from the medical resource database according to the preset search radius.

[0046] The target user's current location is determined by collecting the target user's real-time geographical location information through methods such as GPS positioning, Beidou satellite positioning, mobile communication base station positioning, or Wi-Fi assisted positioning on the terminal. This information can obtain the user's latitude and longitude coordinates, administrative regions such as province, city, district, and street, as well as precise geographical points, thereby determining the target user's current location.

[0047] The preset search radius R is a spatial retrieval threshold pre-configured by the emergency rescue system. It can be flexibly set according to the actual scenario. For example, the preset search radius R can be within 15 kilometers or within 30 kilometers.

[0048] In one possible implementation, the emergency medical system uses the target user's current location as the center and defines a closed spatial search area with a preset search radius R. Then, it traverses the geographical location data of all hospitals in the medical resource database, filters out all hospitals whose geographical location data falls within the spatial search area, and uniformly summarizes and aggregates these hospitals that meet the spatial conditions to form a preliminary candidate hospital set.

[0049] S320. Match the emergency response capabilities required with the emergency response capabilities of each hospital in the preliminary candidate hospital set, and select a set of candidate hospitals whose emergency response capabilities meet the emergency response requirements.

[0050] In one possible approach, emergency medical service (ERS) capability information is collected for each hospital in the initial candidate hospital set. This information includes at least: the hospital's ERS department setup, types and availability of ERS equipment, ERS physician / nurse qualifications, average ERS response time, types of critical and severe illnesses the hospital can handle, ERS transport support capabilities, and the number of available ERS beds. Then, each indicator of the required ERS capability is compared, matched, and verified against the corresponding ERS capability information of each individual hospital in the initial candidate hospital set, completing the matching operation between the required ERS capability and the ERS capability information of each hospital. Next, the hospitals in the initial candidate hospital set are screened and categorized, eliminating hospitals that do not meet the matching criteria, do not meet the required ERS capability, or whose required ERS capability does not match the target needs. Hospitals that are properly matched and whose required ERS capability fully meets or meets the target ERS capability requirements are retained. Finally, from the initial initial candidate hospital set, a final candidate hospital set that meets the target ERS capability requirements is determined. This final candidate hospital set is the standardized and usable candidate hospital set formed after matching the required ERS capability.

[0051] The emergency response method provided in this application uses the target user's current location as the center and a preset search radius to initially screen from the medical resource database to form a preliminary set of candidate hospitals, thereby quickly narrowing the screening scope and improving search efficiency. Then, the emergency response capabilities required are matched and screened with the actual emergency response capabilities of each hospital in the preliminary set of candidate hospitals, which can ensure that the final selected candidate hospitals all have the treatment capabilities to meet the emergency needs of the target user and effectively avoid the mismatch of emergency resources.

[0052] Optionally, the above method matches the required emergency medical capabilities with the emergency medical capabilities of each hospital in the preliminary candidate hospital set, and filters out a set of candidate hospitals whose emergency medical capabilities meet the required emergency medical capabilities, including: For each hospital in the initial candidate hospital set, obtain the deviation of emergency medical services capacity and the number of available bed resources for each hospital.

[0053] The available bed resources refer to the number of dedicated beds in each hospital that correspond to the emergency care capacity.

[0054] In one possible implementation, the emergency response capability deviation term is calculated using the following formula (1) based on the emergency response capability required and the actual emergency response capability of each hospital in the preliminary candidate hospital set.

[0055] Emergency response capability deviation = Actual emergency response capability of each hospital - Emergency response capability required Formula (1) According to the formula (1) above, the emergency response capability deviation term is the deviation between the actual emergency response capability of each hospital and the emergency response capability required for the corresponding severity of illness. If the emergency response capability deviation term is positive, it means that the hospital's actual emergency response capability is higher than the required emergency response capability; if the emergency response capability deviation term is negative, it means that the hospital's actual emergency response capability is lower than the required emergency response capability; if the emergency response capability deviation term is 0, it means that the actual emergency response capability is completely matched with the required emergency response capability.

[0056] In another possible implementation, the evaluation index system for the emergency response capability includes at least: emergency equipment allocation index (e.g., weight 0.3), emergency personnel professional competence index (e.g., weight 0.4), and emergency response efficiency index (e.g., weight 0.3). The emergency equipment allocation index is calculated by weighting the emergency equipment availability rate and emergency equipment coverage rate, and can be calculated using the following formula (2): The score for the emergency medical equipment allocation indicator is calculated as follows: (Emergency medical equipment availability rate × 0.5 + Emergency medical equipment coverage rate × 0.5) × 0.3 Formula (2) Among them, the emergency equipment integrity rate is the percentage of the number of intact emergency equipment to the total number of emergency equipment; the emergency equipment coverage rate is the percentage of the number of types of emergency equipment actually equipped in the hospital to the number of types of emergency equipment stipulated in the region.

[0057] The professional competence index of emergency responders is calculated by weighting the certification rate of emergency responders and the average annual training time of emergency responders, and can be calculated by the following formula (3): The score of the professional competence index of emergency personnel = (the rate of emergency personnel holding certificates × 0.5 + the average annual training time of emergency personnel / the standard training time × 0.5) × 0.4 Formula (3) The certification rate for emergency responders is the percentage of the number of people holding emergency response-related qualification certificates to the total number of emergency responders. The standard training duration is a preset value, such as no less than 80 hours per year.

[0058] The emergency response efficiency index is calculated by weighting the average emergency response time and the response time compliance rate, and can be calculated using the following formula (4): Emergency response efficiency score = ((standard response time - average emergency response time) / standard response time × 0.5 + response time compliance rate × 0.5) × 0.3 Formula (4) The standard response time is a preset value, for example, the standard response time is no more than 15 minutes; the response time compliance rate is the number of emergency rescues with a response time ≤ 15 minutes / the total number of emergency rescues × 100%.

[0059] Based on the scores of the evaluation index system for the three types of emergency response capabilities mentioned above, the comprehensive score P_i of the actual emergency response capability of each preliminary candidate hospital (i represents the i-th preliminary candidate hospital) can be obtained, which can be calculated by the following formula (5): P_i = Emergency equipment allocation score + Emergency personnel professional competence score + Emergency response efficiency score Formula (5) Then, the emergency response capability deviation term ΔP_i is calculated using the following formula (6).

[0060] ΔP_i = |P_i - P_0| Formula (6) Here, P_0 is the standard emergency medical service capability score, which is a spatial retrieval area determined based on a preset search radius R, and the standard emergency medical service capability score corresponding to this spatial retrieval area is obtained. This standard emergency medical service capability score P_0 can be uniformly determined by the health department of this spatial retrieval area, in combination with the overall level of emergency medical service capability and the total amount of emergency medical service demand in the area. For example, the standard emergency medical service capability score P_0 can be set to 85 points (out of 100).

[0061] Among them, the smaller the value of the emergency response capability deviation term ΔP_i obtained according to the above formula (6), that is, the smaller the deviation between the hospital's actual emergency response capability comprehensive score and the standard emergency response capability score, which means that the hospital's actual emergency response capability is more in line with the screening requirements for emergency response capability; if the emergency response capability deviation term ΔP_i exceeds the preset deviation threshold (such as ΔP_max being 10 points), then the hospital does not meet the screening requirements for actual emergency response capability.

[0062] Next, the internal bed capacity of each hospital in the preliminary candidate hospital set is determined. Then, beds corresponding to the emergency care capabilities are selected, meaning these beds must be specifically used to match the emergency care capabilities required for the corresponding disease level at that hospital. For example, if the emergency care capabilities correspond to severe cases, dedicated beds for severe cases within the hospital are selected, excluding general beds, beds for other diseases, and other non-corresponding beds, to ensure the correspondence between beds and emergency care capabilities. Then, the number of currently available dedicated beds is counted, excluding those that are occupied, reserved, faulty, or under maintenance, ensuring that the count represents the available number rather than the total number. This yields the current number of dedicated beds in each hospital in the preliminary candidate hospital set.

[0063] In another possible approach, the total number of emergency-related beds, B_total_i, for each preliminary candidate hospital can be obtained. This total number of emergency-related beds, B_total_i, can be retrieved from the hospital's real-time bed management system to ensure data accuracy and validity. These emergency-related beds must include at least: emergency room beds, ICU (Intensive Care Unit) beds, and dedicated emergency observation beds. It is important to note that these emergency-related beds do not include general inpatient beds or non-emergency specialist beds.

[0064] Then, obtain the number of emergency-related beds currently occupied by each preliminary candidate hospital, B_occupy_i. These currently occupied emergency-related beds include at least the beds occupied by emergency patients who are receiving treatment and waiting to be transferred to other departments, but do not include beds that have been discharged but not vacated. Next, obtain the number of reserved non-emergency beds by each preliminary candidate hospital, B_reserve_i. These reserved non-emergency beds are beds that the hospital reserves to deal with non-emergency emergencies (such as beds reserved for elective surgeries or sudden worsening of the condition of ordinary inpatients). These beds cannot be used to receive emergency patients and must be deducted in full.

[0065] Finally, calculate the number of available bed resources B_i according to the following formula (7).

[0066] B_i=B_total_i - B_occupy_i - B_reserve_i formula (7) According to the formula (7) above, if the calculated result of the available bed resources B_i is negative, it is counted as 0, which means that the hospital currently has no available emergency beds. It should be noted that the available bed resources B_i needs to be updated in real time, with an update frequency of no less than once every 30 minutes, to ensure that the available bed resources B_i used during screening is the latest data, and to avoid deviations in screening results due to data lag.

[0067] In addition, it should be noted that the two operations mentioned above, obtaining the number of available bed resources and the deviation item of emergency response capability, are carried out simultaneously. That is, each hospital in the preliminary candidate hospital set needs to calculate the deviation item of emergency response capability and count the number of available bed resources separately. In the end, a correspondence between a preliminary candidate hospital and a set of emergency response capability deviation items and the number of available bed resources is formed, ensuring that the data matches the preliminary candidate hospitals one by one, without confusion or duplication.

[0068] Based on the deviation of emergency response capabilities and the number of available bed resources, it is determined whether each hospital meets the emergency response requirements.

[0069] In one possible implementation, since the emergency response capability deviation item can objectively quantify the degree of matching between the hospital's actual emergency response capability and the current emergency treatment needs of patients, it can effectively screen out hospitals with insufficient emergency response capability that cannot meet the treatment requirements of patients, or hospitals with excessive emergency response capability that cause unreasonable occupation of medical resources. The number of available bed resources can directly reflect the real-time availability status of emergency beds, ICU beds and other emergency treatment beds in hospitals, and can eliminate hospitals that do not have available beds and cannot receive emergency patients in time. Therefore, the emergency response capability deviation item and the number of available bed resources of each hospital are compared and verified for compliance with the emergency response capability standards determined in the early matching. Through standardized judgment logic, the verification of whether a single hospital meets the emergency response capability requirements is completed, and the accurate judgment of each hospital in the initial candidate hospital set is achieved.

[0070] Hospitals that meet the emergency medical needs will be selected as members of the candidate hospital set.

[0071] In one possible implementation, after assessing the emergency response capabilities and bed availability of each hospital in the initial candidate hospital set, the system will summarize and confirm the assessment results. Only hospitals that pass the emergency response capability deviation check, meet the bed availability requirements, and fully meet the emergency response capability standards corresponding to the current patient's condition level will be formally included and confirmed as members of the candidate hospital set. This ensures that all institutions within the candidate hospital set have both suitable emergency response capabilities and immediate bed availability, providing a compliant and effective basis for subsequent emergency dispatch, comprehensive scoring calculation, and optimal hospital selection.

[0072] The emergency response method provided in this application, for each hospital in the initial candidate hospital set, obtains the corresponding emergency response capability deviation item and the number of available bed resources for each hospital. It can determine whether each hospital meets the required emergency response capabilities from two dimensions: emergency response capability matching degree and real-time supply capacity of dedicated beds. Hospitals that meet the criteria are then identified as members of the candidate hospital set. Therefore, this application can accurately eliminate hospitals whose emergency response capabilities do not match the required emergency response capabilities or whose dedicated bed resources cannot guarantee timely admission, ultimately obtaining a candidate hospital set with suitable treatment capabilities, reliable resource supply, and compliance with emergency admission requirements.

[0073] Optionally, the above method determines whether each hospital meets the emergency response capacity requirements based on the emergency response capacity deviation term and the number of available bed resources, including: When a hospital's emergency response capability deviation is less than or equal to the first threshold and the number of available beds is greater than or equal to the second threshold, the hospital is determined to meet the emergency response capability requirements.

[0074] The first threshold is used to define the minimum emergency response capability necessary for a hospital to handle the stated severity of the illness. It is the emergency response capability deviation threshold ΔP_max, typically set to 10 points. The second threshold is used to ensure that the target hospital has at least one immediately usable dedicated bed when receiving patients. It is the minimum number of available beds, B_min, which can be selected based on the urgency of the emergency need and the overall reserve of regional bed resources. For example, when screening for a single emergency case, the minimum number of available beds, B_min, is 2; when screening for a batch of emergency cases (e.g., ≥3 people), the minimum number of available beds, B_min, is 5. For ease of discussion, this screening follows the default single emergency case standard, i.e., a minimum number of available beds, B_min, of 2.

[0075] In one possible implementation, for each hospital H_i in the initial candidate hospital set H, its emergency response capability deviation ΔP_i is checked sequentially to see if it is less than or equal to the emergency response capability deviation threshold ΔP_max (e.g., 10 points). If the emergency response capability deviation ΔP_i is greater than the emergency response capability deviation threshold ΔP_max (e.g., 10 points), the hospital is determined not to meet the emergency response capability requirements and is not included in the subsequent process. Then, for hospitals whose emergency response capability deviation ΔP_i is less than or equal to the emergency response capability deviation threshold ΔP_max (e.g., 10 points), their available bed resources B_i are checked to see if it is greater than or equal to B_min (e.g., 2 beds). If the available bed resources B_i is less than the minimum number of available beds (e.g., 2 beds), the hospital is determined not to meet the emergency response capability requirements; if the available bed resources B_i is greater than or equal to the minimum number of available beds (e.g., 2 beds), the hospital is confirmed to meet the emergency response capability requirements and is included in the candidate hospital set. Finally, all hospitals that have been determined to meet the emergency care requirements are compiled into a final candidate hospital set, denoted as H_select={H_i | H_i∈H, ΔP_i≤10 points, B_i≥2 sheets}.

[0076] It should be noted that if the candidate hospital set is empty after screening, the screening threshold needs to be lowered. For example, the threshold for the emergency response capability deviation ΔP_max should be adjusted to 15 points, and the minimum number of available beds B_min should be adjusted to 1 bed, and the judgment process should be re-executed. If the number of candidate hospitals exceeds 10 after screening, they should be sorted in ascending order by the emergency response capability deviation ΔP_i, and the top 10 should be selected as the final candidate hospital set. If the emergency response capability deviation ΔP_i is the same, they should be sorted in descending order by the number of available beds B_i, and the top 10 should be selected as the final candidate hospital set. At the same time, a screening report should be generated based on the judgment results, clearly showing the emergency response capability deviation ΔP_i, the number of available beds B_i, and the screening result (if satisfied / not satisfied) for each hospital. This facilitates the retention of the screening report for future reference and data traceability.

[0077] The emergency response method provided in this application determines whether a hospital meets the required emergency response capabilities by individually judging whether the deviation of its emergency response capabilities is less than or equal to a first threshold and whether the number of available beds is greater than or equal to a second threshold. This eliminates unqualified hospitals with mismatched emergency response capabilities and a shortage of beds, and only hospitals that meet the criteria are included in the final candidate hospital set. This ensures that all candidate hospitals included in the subsequent evaluation have appropriate emergency response capabilities and sufficient available beds, effectively improving the accuracy of emergency hospital selection and the feasibility of subsequent treatment and dispatch.

[0078] Figure 4 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 3 .like Figure 4 As shown, the above method calculates a comprehensive score between the candidate hospital and the target user for each candidate hospital in the candidate hospital set, including: S410. Based on the location of each candidate hospital in the candidate hospital set and the current location of the target user, determine the real-time traffic information and route information from the target user to each candidate hospital.

[0079] The location of each candidate hospital in the candidate hospital set is extracted by the emergency medical system from the feature data of the pre-stored candidate hospital set. That is, the location data of each candidate hospital in the candidate hospital set is extracted, and the location data format is usually latitude and longitude coordinates.

[0080] In one possible implementation, the emergency medical system calls a third-party real-time traffic API (such as the traffic API of Gaode Maps or Baidu Maps), using the target user's current location (i.e., the latitude and longitude of the starting point) and the location of a single candidate hospital (i.e., the latitude and longitude of the destination) as API request parameters. This third-party real-time traffic API returns real-time traffic data for all road segments along the route between the two points. The emergency medical system parses the returned traffic data, extracting key information, which includes at least: the traffic status of each road segment (e.g., smooth, slow, congested, severely congested), real-time traffic speed, congestion duration, and abnormal information affecting traffic such as construction / accidents, to facilitate subsequent route optimization and travel time estimation. Finally, the parsed real-time traffic information is associated with and stored with the candidate hospital. This process is repeated for all candidate hospitals to determine the real-time traffic information for each candidate hospital. The call frequency of the third-party real-time traffic API can be set according to requirements, with a minimum update every 30 seconds.

[0081] In one possible implementation, based on the target user's current location (i.e., the starting point) and the location of a single candidate hospital (i.e., the destination), combined with the aforementioned real-time traffic data, the optimal route is calculated using a preset route planning algorithm (such as the A* route planning algorithm). During route planning, the shortest real-time travel time is the optimization objective, while secondary objectives such as shortest distance and least congestion can be used as supplementary references for the target user's selection. This preset route planning algorithm incorporates real-time traffic conditions of the traversed road segments (e.g., increased weight for congested road segments and decreased weight for uncongested road segments) to calculate the complete route from the starting point to the destination. This route information includes at least: the names of the traversed road segments, the order of the road segments, turning prompts for each road segment (e.g., turn left into XX Road 500 meters ahead), the total route distance, the estimated real-time travel time, the coordinates of the starting point and destination, and the coordinates of key nodes along the route (e.g., intersections, bridges). Finally, after calculating the path information for a single candidate hospital, the set of candidate hospitals is traversed, and the above algorithm calculation process is repeated to determine the path information from the target user to each candidate hospital. Finally, the location of each candidate hospital, the corresponding real-time traffic information, and the corresponding path information are associated and integrated.

[0082] In another possible implementation, a pre-entered map database can be used, which stores static route information for all roads within the target user's area. The basic data for the corresponding route can be extracted through an interface called by the emergency medical system to obtain the route information.

[0083] S420 calculates the estimated travel time based on route information, real-time traffic information, and a preset route planning algorithm.

[0084] The preset path planning algorithm can be selected according to the actual situation. For example, the preset path planning algorithm can be Dijkstra's algorithm, A* algorithm, dynamic programming algorithm, etc. In one possible implementation, the emergency medical system first invokes a pre-defined algorithm module. This module uses pre-collected data (such as road segment mileage and road type) and real-time traffic information (such as traffic speed and congestion coefficient) as input parameters for the pre-defined route planning algorithm. The algorithm then combines the base speed corresponding to the road type and the speed reduction factor corresponding to the congestion level to calculate the travel time for each road segment. It calculates the travel time for each segment individually, while also considering the time spent connecting road segments (such as turning at intersections and waiting at traffic lights), thus completing a preliminary calculation of the overall route travel time. After the pre-defined route planning algorithm completes the calculation and summary of the travel time for each road segment, the emergency medical system performs a secondary verification of the results. This verification includes at least the following: the completeness of the route information (e.g., whether there are any unrecorded road segment mileages) and the timeliness of the real-time traffic information (e.g., whether there are any congestion events that have not been updated in a timely manner). If an anomaly is detected, the preset route planning algorithm is called again for calculation. If the verification is correct, the time taken for each road segment and the connection time calculated by the preset route planning algorithm are summarized to obtain the total estimated travel time for the entire route. At the same time, a slight correction is made by combining historical travel data (such as past travel times under the same route and road conditions) to ensure the accuracy of the estimated result. Finally, the emergency rescue system outputs the corrected total time as the estimated travel time to the user or subsequent related modules.

[0085] S430. Query the real-time load index of each candidate hospital from the medical resource database.

[0086] In one possible implementation, since the medical resource database consists of data reported in real-time by candidate hospitals through a designated interface, and this database is updated every 5 minutes to ensure the real-time nature and accuracy of the data, a preset data query interface is used to extract all real-time medical resource data for each candidate hospital in the selected candidate hospital set from the aforementioned medical resource database in batches. This real-time medical resource data refers to the real-time data related to beds, medical staff, equipment, and patients stored in the medical resource database for that hospital. During the extraction process, the unique identifier (such as hospital code) of each candidate hospital is automatically matched to ensure that the data for each candidate hospital is not confused or omitted. It should be noted that if the extracted real-time medical resource data is missing or incorrect (such as a negative number of beds or the number of equipment in use exceeding the total number), an early warning will be triggered immediately, and the data will be automatically re-extracted. If the extraction fails three times consecutively, the candidate hospital's data is marked as abnormal, and its real-time load index will not be calculated.

[0087] The real-time load index L_i can be calculated according to the following formula (8).

[0088] Real-time load index L_i = α×(current number of occupied critical care beds / total number of critical care beds) + β×(current number of on-duty critical care medical staff / total number of critical care medical staff) + γ×(current number of occupied critical care equipment / total number of critical care equipment) + δ×(current number of hospitalized critical care patients / total number of critical care beds) Formula (8) Wherein, α, β, γ, and δ are preset weighting coefficients, and α+β+γ+δ=1. Each preset weighting coefficient can be divided according to the importance of medical resources. For example, α=0.4 mainly reflects the bed load; β=0.3 reflects the medical staff load; γ=0.2 reflects the equipment load; and δ=0.1 reflects the patient quantity load. It should be noted that all data are retained to two decimal places in the calculation process to ensure calculation accuracy.

[0089] The calculated real-time load index L_i for each candidate hospital is compared with a preset load index range. This preset range is typically set to 0 to 1, where 0 represents no load, 1 represents full load, and anything above 1 indicates overload. If the real-time load index L_i falls within the preset range, it is confirmed as the real-time load index for that candidate hospital. If the real-time load index L_i exceeds the preset range, it is considered a calculation anomaly. The data extraction and calculation process is automatically traced back to check data accuracy and the correctness of formula substitutions. The calculation is then corrected and recalculated until a real-time load index L_i conforming to the preset range is obtained. If the anomaly persists after multiple corrections, the hospital's load index is marked as abnormal and synchronously reported to the database management system.

[0090] S440. Based on the severity of the illness, adjust the weights of the estimated travel time, real-time load index, emergency response capacity deviation, and available bed resources.

[0091] The estimated travel time is the estimated travel time from the patient's current location to the target candidate hospital, in minutes; the real-time load index is a quantitative indicator reflecting the current workload of the target candidate hospital; the emergency response capability deviation refers to the difference between the actual emergency response capability of the target candidate hospital and the required emergency response capability, used to measure the degree to which the candidate hospital's emergency response capability meets the standards; the number of available bed resources refers to the number of beds in the target candidate hospital that can be immediately allocated and adapted to the patient's condition level, excluding beds that are already reserved, occupied, or under maintenance.

[0092] In one possible implementation, based on the disease severity levels determined in Table 1 above, a disease severity level adjustment coefficient with weights can be preset for the estimated travel time according to different disease severity levels. For example, the disease severity level for Level 1 is set to 4; the disease severity level adjustment coefficient for Level 2 is set to 3; and the disease severity level adjustment coefficient for Level 3 is set to 2. The weight of the estimated travel time can then be adjusted using the following formula (9), thus obtaining the adjusted estimated travel time weight W1.

[0093] Adjusted estimated travel time weight W1 = base weight × disease severity adjustment coefficient Formula (9) The basic weight for the preset estimated travel time is generally set to 0.2.

[0094] According to the formula (9) above, the higher the severity of the illness, the higher the weight of the estimated travel time W1. For example, Level 1 corresponds to critically ill patients, and the adjusted weight of the estimated travel time W1 is 0.2×4=0.8. This means that the estimated travel time has a greater impact on the overall score, and speed is given more importance. That is, candidates with shorter travel times are selected first to avoid delaying the treatment of critically ill patients due to excessively long estimated travel times. The lower the severity of the illness, the lower the weight of the estimated travel time W1. For example, Level 3 corresponds to mild patients, and the adjusted weight of the estimated travel time W1 is 0.2×2=0.4. This reduces the impact of the estimated travel time on the overall score, and there is no need to excessively pursue short travel times. Emergency transport capacity is allocated reasonably.

[0095] In one possible implementation, based on the disease severity levels determined in Table 1 above, the real-time load index can be adjusted by setting disease severity level adjustment coefficients with different weights according to different disease severity levels. For example, the disease severity level of Level 1 is set to 0.22; the disease severity level of Level 2 is set to 0.66; and the disease severity level of Level 3 is set to 1. The weight of the real-time load index can then be adjusted using the following formula (10) to obtain the adjusted real-time load index weight W2.

[0096] Adjusted real-time load index weight W2 = base weight × disease severity adjustment coefficient Formula (10) The basic weight of the preset real-time load index is generally set to 0.3.

[0097] According to the formula (10) above, the higher the severity of the illness, the lower the patient's tolerance to the real-time workload index of the candidate hospital. For example, Level 1 corresponds to critically ill patients, and the adjusted real-time workload index weight W2 is 0.3 × 0.22 = 0.0066. This means that by increasing the weight of the real-time workload index W2, its proportion in the overall score can be strengthened, thereby quickly identifying candidate hospitals with lower real-time workload indices (i.e., more abundant medical resources) and prioritizing the treatment needs of critically ill patients. The lower the disease level, the higher the patient's tolerance for the real-time workload index of the candidate hospitals. For example, Level 3 corresponds to general mild cases, and the adjusted weight of the real-time workload index W2 is 0.3 × 1 = 0.3. This means that by reducing the weight of the real-time workload index W2, the impact of the real-time workload index on the overall score can be reduced, avoiding the waste of medical resources due to excessive focus on the real-time workload index.

[0098] It should be noted that if the real-time load index fluctuates abnormally after the real-time load index weight W2 is adjusted, the accuracy of the real-time load index should be checked first before adjusting the real-time load index weight W2.

[0099] In one possible implementation, based on the severity levels determined in Table 1 above, the emergency response capability deviation item can be adjusted by setting a severity level adjustment coefficient according to different severity levels. For example, the severity level adjustment coefficient for Level 1 is set to 0.28; for Level 2, it is set to 0.6; and for Level 3, it is set to 0.85. The weight of the emergency response capability deviation item can then be adjusted using the following formula (11) to obtain the adjusted emergency response capability deviation item weight W3.

[0100] Adjusted emergency response capability deviation weight W3 = Basic weight × Severity severity adjustment coefficient Formula (11) The basic weight of the preset emergency rescue capability deviation item is generally set to 0.25.

[0101] According to the formula (11) above, the higher the severity of the illness, the higher the requirements for the emergency medical capabilities of the candidate hospitals. For example, Level 1 corresponds to critically ill patients, and the adjusted weight W3 for the emergency medical capability deviation item is 0.25 × 0.28 = 0.07. Therefore, it is necessary to focus on the deviation of emergency medical capabilities. Thus, the weight W3 for this emergency medical capability deviation item is increased so that the comprehensive score focuses more on the matching degree of emergency medical capabilities, ensuring that critically ill patients can be matched with candidate hospitals whose emergency medical capabilities meet or exceed the standards. The lower the severity of the illness, the lower the requirements for emergency medical capabilities for mild patients. For example, Level 3 corresponds to general mild patients, and the adjusted weight W3 for the emergency medical capability deviation item is 0.25 × 0.85 = 0.2125. If the deviation in emergency response capability is not properly addressed, the weight W3 of this deviation item should be reduced to decrease its impact on the overall score and avoid excluding suitable candidate hospitals due to minor deviations in emergency response capability.

[0102] In one possible implementation, based on the disease severity levels determined in Table 1 above, the number of available bed resources can be adjusted using disease severity level adjustment coefficients with different weights according to different disease severity levels. For example, the adjustment coefficient for Level 1 is set to 0.28; for Level 2, it is set to 0.2; and for Level 3, it is set to 0.4. The weight of the number of available bed resources can then be adjusted using the following formula (12) to obtain the adjusted weight W4 of the number of available bed resources.

[0103] Adjusted bed availability weight W4 = Basic weight × Disease severity adjustment coefficient Formula (12) The basic weight of the number of available bed resources is generally set to 0.25.

[0104] According to the formula (12) above, the higher the severity of the illness, the greater the urgency of bed resources. For example, critically ill patients corresponding to Level 1 need to be admitted to ICU beds immediately for treatment. Delay may endanger their lives. Therefore, the weight of available bed resources is increased so that the comprehensive score takes bed availability into priority, ensuring that critically ill patients can be quickly matched with candidate hospitals with available beds. For example, for critically ill patients corresponding to Level 1, the adjusted weight of available bed resources W4 is 0.25 × 0.28 = 0.07. The lower the severity of the illness, the less urgency of bed resources. It is appropriate to wait for bed turnover. Therefore, the weight of available bed resources W4 is reduced to avoid affecting the reasonable allocation of mild patients due to temporary bed shortages. For example, for general mild patients corresponding to Level 3, the adjusted weight of available bed resources W4 is 0.25 × 0.4 = 0.1.

[0105] It should be noted that the number of available beds needs to be updated in real time (e.g., every 10 minutes). If the number of available beds for a candidate hospital becomes 0, the emergency system needs to automatically lower the overall score of that candidate hospital to avoid assigning patients to candidate hospitals with no available beds. At the same time, after the weight adjustment, it is necessary to ensure that the sum of the weights of the estimated travel time, real-time load index, emergency response capability deviation item, and number of available beds in the overall score is 1 (i.e., W1+W2+W3+W4=1). If the sum of weights is abnormal, the emergency system needs to automatically trigger calibration to ensure the rationality of the overall scoring logic.

[0106] S450, based on the adjusted weights, estimated travel time, real-time load index, emergency response capability deviation item, and available bed resources, a weighted calculation is performed to obtain the comprehensive score for each candidate hospital.

[0107] In one possible implementation, the comprehensive score is determined using the following formula (13) based on the adjusted weights, estimated travel time, real-time load index, emergency response capacity deviation term, and available bed resources. .

[0108] Formula (13) in, For estimated travel time; This is the real-time load index; This is a deviation item in emergency response capability; i represents the number of available bed resources. As a safety factor, it is generally set to 0.1 to prevent division by zero.

[0109] The emergency response method provided in this application determines the real-time traffic and route information from the target user to each candidate hospital based on the location information of each candidate hospital in the candidate hospital set and the current location information of the target user. Then, by combining the route information and real-time traffic information and using a preset route planning algorithm, the estimated travel time from the target user to each candidate hospital is calculated to ensure the real-time and accuracy of the travel time calculation. The real-time load index of each candidate hospital is retrieved and determined based on the medical resource database to achieve an objective quantification of the current patient reception pressure of the hospital. Subsequently, according to the severity of the target user's condition, the weights of the estimated travel time, real-time load index, emergency response capacity deviation item, and available bed resources in the comprehensive score are dynamically adjusted to ensure that the weight configuration in the comprehensive score is adapted to the needs of different emergency scenarios. Finally, the comprehensive score of each candidate hospital is obtained by weighted calculation based on the adjusted weights and the above-mentioned quantitative indicators, thereby achieving a standardized quantitative assessment of the adaptation cost of emergency hospitals.

[0110] Optionally, the above method adjusts the weights of the estimated travel time, real-time load index, emergency response capacity deviation, and available bed resources based on the severity of the illness, including: If the severity of the illness is Level 1, then the weight of the estimated travel time is greater than the weight of any one of the following: real-time load index, emergency response capacity deviation, and bed availability.

[0111] In one possible implementation, if the illness level is Level 1, corresponding to critically ill patients, these patients have extremely urgent conditions, require very timely treatment, and have very low tolerance for the real-time workload of candidate hospitals. Prioritizing transfer efficiency and timely treatment is crucial. Therefore, the weight of estimated travel time (W1) should be set higher than any one of the following: the weight of real-time workload index (W2), the weight of emergency response capability deviation (W3), and the weight of available bed resources (W4). This ensures that the estimated travel time accounts for the highest proportion in the overall score, and thus, during hospital selection, candidate hospitals with shorter estimated travel times are prioritized, guaranteeing that critically ill patients can be quickly transferred to the target medical institution for treatment. For example, according to Table 1 above, the weights of real-time workload index (W2), emergency response capability deviation (W3), and available bed resources (W4) are all the same.

[0112] If the severity of the illness is Level 2, the weight of the estimated travel time configuration is the same as the weight of the real-time load index configuration, and the weights of the emergency response capability deviation item and the number of available bed resources are both less than the weight of the estimated travel time.

[0113] In one possible implementation, if the illness level is Level 2, corresponding to emergency patients, these patients have urgent conditions and require high timeliness of treatment. At the same time, there are also certain requirements for the real-time load status of candidate hospitals. It is necessary to balance the transfer efficiency and the capacity of medical institutions. In this case, the weight of the estimated travel time W1 and the weight of the real-time load index W2 should be the same or similar. The weights of the emergency response capability deviation item W3 and the number of available beds W4 should be less than the weight of the estimated travel time W1. This will ensure that the estimated travel time and the real-time load index have the highest proportions in the comprehensive score. Then, in the process of selecting candidate hospitals, priority will be given to matching candidate hospitals with shorter estimated travel times and better real-time load indices, so as to ensure that emergency patients can be transferred to suitable medical institutions for treatment in a timely manner.

[0114] If the severity of the illness is level three, the weight of the estimated travel time is less than the weight of the real-time load index, and the sum of the weights of the emergency response capability deviation item and the number of available bed resources is the same as the weight of the real-time load index.

[0115] In one possible implementation, if the illness level is Level 3, corresponding to mild cases, these patients have relatively stable conditions, lower requirements for treatment timeliness, and relatively high tolerance for the real-time load of candidate hospitals. Therefore, there is no need to prioritize the speed of transport. In this case, the weight of the estimated travel time W1 should be set to be less than the weight of the real-time load index W2. At the same time, the sum of the weights of the emergency response capability deviation item W3 and the weight of the number of available beds W4 should be equal to the weight of the real-time load index W2. This will result in a higher proportion of real-time load index, emergency response capability, and bed resources in the comprehensive score. Consequently, during the candidate hospital selection process, priority will be given to matching candidate hospitals with lower real-time load index, suitable emergency response capability, and sufficient bed resources, so as to rationally allocate medical resources to meet the treatment needs of mild cases.

[0116] The emergency response method provided in this application assigns differentiated weights to four indicators—estimated travel time, real-time load index, emergency response capacity deviation, and available bed resources—for different severity levels of illness. When the severity level is Level 1, the weight of estimated travel time is greater than the weight of any one of the real-time load index, emergency response capacity deviation, or available bed resources, prioritizing the rapid transfer of critically ill patients and minimizing emergency response time. When the severity level is Level 2, estimated travel time and the real-time load index are assigned equal weights, and both are greater than the weights of the emergency response capacity deviation and available bed resources, balancing emergency transport efficiency with the hospital's real-time capacity to ensure balanced and stable emergency dispatch. When the severity level is Level 3, the weight of estimated travel time is less than the weight of the real-time load index, and the sum of the weights of the emergency response capacity deviation and available bed resources is consistent with the weight of the real-time load index, emphasizing the matching of the hospital's real-time load with bed and emergency response capacity, improving the accuracy of emergency resource allocation and overall utilization efficiency.

[0117] Figure 5 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 4 .like Figure 5 As shown, the above method determines the target hospital from the candidate hospital set based on the comprehensive score of each candidate hospital, including: S510. Select the comprehensive score with the lowest value from the comprehensive scores of each candidate hospital.

[0118] In one possible implementation, the comprehensive score of each candidate hospital is first calculated using the formula (13) above. Then, the comprehensive scores of all candidate hospitals are centrally sorted and summarized to form a filterable comprehensive score dataset. Then, the comprehensive score dataset after sorting and summarizing is sorted and filtered using a sorting method (such as manual sorting or automatic sorting by the emergency system) to arrange all comprehensive scores in ascending order. After sorting, the data at the top of the sorted results is selected, that is, the comprehensive score with the smallest value among multiple comprehensive scores is selected.

[0119] It should be noted that the higher the severity of the illness (e.g., Level 1 corresponds to critically ill patients), the lower the patient's tolerance for the real-time workload index of candidate hospitals, and the more necessary it is to match them with candidate hospitals with low real-time workload index (i.e., sufficient medical resources). Conversely, the smaller the comprehensive score, the lower the real-time workload index and the more sufficient the resources of the corresponding candidate hospital. Therefore, selecting the comprehensive score with the smallest value is essentially about quickly identifying the candidate hospital with the most sufficient resources and the best match for the patient's condition through numerical comparison. During the screening process, it is necessary to ensure that all comprehensive scores are compared and that no candidate hospital's score data is missed, to avoid unreasonable patient allocation due to screening omissions.

[0120] S520. The candidate hospital with the lowest comprehensive score is selected as the target hospital.

[0121] In one possible approach, a smaller comprehensive score indicates a lower real-time workload index for the candidate hospital (i.e., more abundant medical resources), making it more suitable for patients with high disease severity and low tolerance to the real-time workload index (such as critically ill patients corresponding to Level 1). This screening method can quickly identify candidate hospitals (i.e., target hospitals) with sufficient resources that are most suitable for patient treatment, prioritizing patient admission needs and ensuring that the comprehensive score accurately reflects the suitability of the hospital.

[0122] The emergency response method provided in this application filters the comprehensive scores of multiple candidate hospitals, extracts the comprehensive score with the lowest value, and then uses this lowest comprehensive score as the criterion to accurately determine the target hospital from the candidate hospital set. Therefore, the screening logic for determining the target hospital in this application can avoid the bias caused by subjective human judgment, while quickly identifying the optimal candidate hospital that meets the comprehensive score requirements, effectively improving the efficiency and accuracy of target hospital determination.

[0123] Figure 6 A flowchart illustrating an emergency response method provided in this application embodiment. Figure 5 .like Figure 6 As shown, the method described above, in response to identifying an abnormal event in the target user, determines the target user's disease severity level, including: S610, In response to the identification of an abnormal event of the target user, determine the corresponding multiple physiological data.

[0124] In one possible implementation, the emergency medical services system identifies users who require monitoring and may exhibit health abnormalities (such as patients wearing monitoring devices or other health monitoring subjects) as target users. Through pre-set monitoring mechanisms (such as smart wearable devices, medical monitoring instruments, and remote monitoring systems), it captures the target user's status in real time. When the monitored target user's status deviates from the normal range (such as fainting, abnormal facial color, spontaneous discomfort, or sudden fluctuations in baseline data collected by the monitoring device), an abnormal event is identified. This identification can be achieved through real-time data collection by device sensors, user-initiated triggering (such as a one-click emergency call), or automatic judgment by the emergency medical services system algorithm (such as data exceeding the normal baseline). After identifying an abnormal event, a data retrieval or supplementary collection process is immediately initiated to determine multiple physiological data corresponding to the abnormal event. This means acquiring physiological indicator data associated with the abnormal event and reflecting the user's health status, rather than random collection. For example, the emergency medical system can access the target user's physiological data stored in the monitoring equipment, or trigger the equipment to collect relevant physiological data in real time. For example, if the abnormal event is "sudden increase in heart rate", the corresponding physiological data can include at least: heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate, etc., to ensure that the collected physiological data is related to the abnormal event and to provide support for subsequent judgment of the condition.

[0125] S620. Compare each physiological data point with the preset disease threshold data.

[0126] Among them, the preset condition threshold data are various physiological indicator thresholds that are preset in advance by the emergency system to judge the severity of the condition. These threshold data need to be preset in combination with medical clinical standards and pathological research data, and each physiological data (such as heart rate) corresponds to at least one set of preset condition threshold data (such as normal threshold, mild abnormal threshold, moderate abnormal threshold, and severe abnormal threshold of heart rate). That is, multiple physiological data correspond to multiple sets of preset condition threshold data.

[0127] In one possible approach, each piece of physiological data is compared and analyzed one by one with its corresponding preset disease threshold data. For example, the collected target user's heart rate data (e.g., 130 beats / min) is compared with preset heart rate disease thresholds (e.g., normal 60-100 beats / min, mild abnormality 101-120 beats / min, moderate abnormality 121-140 beats / min) to determine which preset disease threshold range the physiological data falls into. At the same time, the comparison process between all multiple physiological data and their corresponding preset disease threshold data is completed one by one to ensure a comprehensive comparison between multiple physiological data and multiple preset disease threshold data.

[0128] S630. Based on the comparison results, determine the severity level of the target user's condition.

[0129] In one possible implementation, all comparison results are obtained after comprehensively comparing multiple physiological data with multiple preset condition threshold data. These comparison results include at least: the threshold range judgment for each physiological data (e.g., heart rate within a moderately abnormal range, blood pressure within a slightly abnormal range, blood oxygen saturation within a normal range, etc.). To implement this, all comparison results must first be summarized, categorized, and statistically analyzed (e.g., the number of abnormal physiological data, the threshold levels corresponding to each type of abnormal physiological data, etc.) to ensure that no comparison result for any physiological data is missed. Then, according to the pre-set condition level judgment rules of the emergency medical system corresponding to the comparison results, where the preset condition levels include at least: mild, moderate, and severe, the corresponding preset condition level judgment rules can be set as follows: if only one physiological data is within the slightly abnormal threshold range and the rest are normal, it is judged as a mild condition; if 2-3 physiological data are within the slightly abnormal threshold range, or one physiological data is within the moderate abnormal threshold range, it is judged as a moderate condition; if 3 or more physiological data are within the abnormal threshold range, or at least one physiological data is within the severe abnormal threshold range, it is judged as a severe condition. Finally, by combining the summarized comparison results and referring to the preset disease level determination rules, a comprehensive analysis and determination are carried out to finally obtain the unique disease level corresponding to the target user. For example, if the summarized comparison results are "moderate abnormal heart rate, mild abnormal blood pressure, and normal blood oxygen saturation", the corresponding disease level can be determined as moderate by referring to the preset disease level determination rules, so as to ensure that the determination of the disease level is directly related to the comparison results.

[0130] The emergency response method provided in this application responds to the identification of abnormal events of a target user, determines multiple corresponding physiological data, compares each physiological data with preset disease threshold data, and objectively determines the disease level of the target user based on the comparison results. This can effectively avoid the subjective bias of manual assessment, realize the quantification and accurate classification of disease levels, and improve the response efficiency and judgment reliability of disease assessment under abnormal events.

[0131] Based on the same inventive concept, this application also provides an emergency response device. Since the principle of the device in this application is similar to the emergency response method described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0132] Figure 7 This is a schematic diagram of the structure of an emergency response device provided in an embodiment of this application. Figure 7 As shown, the emergency response device 700 may include: The first determining module 701 is used to determine the severity level of the target user's illness in response to the detection of an abnormal event of the target user; The matching module 702 is used to match the emergency response capabilities corresponding to the pre-defined mapping relationship between the severity of the illness and the emergency response capabilities required. Filtering module 703 is used to filter a set of candidate hospitals that meet the emergency care requirements from the medical resource database. The calculation module 704 is used to calculate the comprehensive score between the candidate hospital and the target user for each candidate hospital in the candidate hospital set; the comprehensive score is used to characterize the expected emergency cost of sending the target user to each candidate hospital; The second determination module 705 is used to determine the target hospital from the set of candidate hospitals based on the comprehensive score of each candidate hospital, and send the target user's medical information to the target hospital to trigger the target hospital's emergency resource preparation process.

[0133] In one optional implementation, the filtering module 702 is specifically used to: select a preliminary set of candidate hospitals from the medical resource database with the current location of the target user as the center and a preset search radius; match the emergency care capabilities required with the emergency care capabilities of each hospital in the preliminary set of candidate hospitals, and filter out a set of candidate hospitals whose emergency care capabilities meet the emergency care requirements.

[0134] In one optional implementation, the screening module 702 is specifically used to: for each hospital in the preliminary candidate hospital set, obtain the emergency response capability deviation item and the number of available bed resources for each hospital; wherein, the emergency response capability deviation item is used to characterize the deviation between the actual emergency response capability of each hospital and the capability required for emergency response; the number of available bed resources is the number of currently dedicated beds in each hospital corresponding to the capability required for emergency response; based on the emergency response capability deviation item and the number of available bed resources, determine whether each hospital meets the capability required for emergency response; and determine the hospitals that meet the capability required for emergency response as members of the candidate hospital set.

[0135] In one optional implementation, the screening module 702 is specifically used to: determine that each hospital meets the emergency response requirements when the deviation of each hospital's emergency response capability is less than or equal to a first threshold and the number of available bed resources is greater than or equal to a second threshold.

[0136] In one optional implementation, the calculation module 703 is specifically used to: determine the real-time traffic information and path information from the target user to each candidate hospital based on the location of each candidate hospital in the candidate hospital set and the current location of the target user; calculate the estimated travel time based on the path information, real-time traffic information, and a preset path planning algorithm; query the real-time load index of each candidate hospital from the medical resource database; adjust the weights corresponding to the estimated travel time, real-time load index, emergency response capability deviation item, and available bed resources based on the severity of the illness; and perform a weighted calculation on the estimated travel time, real-time load index, emergency response capability deviation item, and available bed resources based on the adjusted weights to obtain a comprehensive score for each candidate hospital.

[0137] In one optional implementation, the calculation module 703 is specifically configured to: if the illness level is Level 1, then the weight of the estimated travel time is greater than the weight of any one of the real-time load index, the emergency response capability deviation item, and the number of available bed resources; if the illness level is Level 2, then the weight of the estimated travel time is the same as the weight of the real-time load index, and the weight of the estimated travel time is greater than the weight of the emergency response capability deviation item and the number of available bed resources; if the illness level is Level 3, then the weight of the estimated travel time is less than the weight of the real-time load index, and the sum of the weights of the emergency response capability deviation item and the number of available bed resources is the same as the weight of the real-time load index.

[0138] In one optional implementation, the second determining module 704 is specifically used to: select the comprehensive score with the smallest value from the comprehensive scores of each candidate hospital; and determine the candidate hospital corresponding to the comprehensive score with the smallest value as the target hospital.

[0139] In one optional implementation, the first determining module 701 is specifically used to: in response to identifying an abnormal event of the target user, determine a plurality of corresponding physiological data; compare each physiological data with preset disease threshold data; and determine the disease level of the target user based on the comparison results.

[0140] It should be noted that for details not disclosed in the emergency response device of this application embodiment, please refer to the details disclosed in the emergency response method of this application embodiment, which will not be repeated here.

[0141] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0142] Optionally, embodiments of this application also provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor performs the steps of the emergency response method for the removable storage medium described in the above embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the functional units in the various embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0144] Optionally, this embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement an emergency response method provided in the above embodiment.

[0145] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0146] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0147] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0148] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An emergency response method, characterized in that, include: In response to the identification of abnormal events in the target user, the severity of the target user's condition is determined; From the preset mapping relationship between disease severity levels and emergency response capabilities, the emergency response capabilities corresponding to the disease severity level are matched and obtained. A set of candidate hospitals that meet the aforementioned emergency medical needs are selected from the medical resource database; For each candidate hospital in the candidate hospital set, a comprehensive score is calculated between the candidate hospital and the target user; the comprehensive score is used to characterize the expected emergency medical costs of sending the target user to each candidate hospital; Based on the comprehensive score of each candidate hospital, a target hospital is determined from the set of candidate hospitals, and the target user's medical condition information is sent to the target hospital to trigger the target hospital's emergency resource preparation process.

2. The method according to claim 1, characterized in that, The set of candidate hospitals selected from the medical resource database that meet the emergency care requirements includes: Using the target user's current location as the center, a preliminary set of candidate hospitals is selected from the medical resource database according to a preset search radius; The required emergency medical capabilities are matched with the emergency medical capabilities of each hospital in the preliminary candidate hospital set to select the candidate hospital set whose emergency medical capabilities meet the required emergency medical capabilities.

3. The method according to claim 2, characterized in that, The step of matching the required emergency medical capabilities with the emergency medical capabilities of each hospital in the preliminary candidate hospital set, and selecting the candidate hospital set whose emergency medical capabilities meet the required emergency medical capabilities, includes: For each hospital in the preliminary candidate hospital set, obtain the emergency medical service capacity deviation term and the number of available bed resources for each hospital; wherein, the emergency medical service capacity deviation term is used to characterize the deviation between the actual emergency medical service capacity of each hospital and the required emergency medical service capacity; the number of available bed resources is the number of currently dedicated beds in each hospital corresponding to the required emergency medical service capacity; Based on the emergency medical service capacity deviation item and the number of available bed resources, it is determined whether each hospital meets the emergency medical service capacity requirements; Hospitals that meet the emergency medical needs will be identified as members of the candidate hospital set.

4. The method according to claim 3, characterized in that, The process of determining whether each hospital meets the required emergency care capacity based on the emergency care capacity deviation item and the number of available bed resources includes: When each hospital has an emergency response capability deviation that is less than or equal to a first threshold and has a number of available beds that is greater than or equal to a second threshold, then each hospital is determined to meet the emergency response capability requirements.

5. The method according to claim 1, characterized in that, For each candidate hospital in the candidate hospital set, the comprehensive score between the candidate hospital and the target user is calculated, including: Based on the location of each candidate hospital in the candidate hospital set and the current location of the target user, determine the real-time traffic information and path information from the target user to each candidate hospital; Based on the path information, the real-time traffic information, and the preset path planning algorithm, the estimated travel time is calculated; From the medical resource database, query the real-time load index of each candidate hospital; Based on the severity of the illness, the weights corresponding to the estimated travel time, the real-time load index, the emergency response capacity deviation item, and the number of available bed resources are adjusted. Based on the adjusted weights, the estimated travel time, the real-time load index, the emergency response capability deviation, and the number of available bed resources are weighted and calculated to obtain a comprehensive score for each candidate hospital.

6. The method according to claim 5, characterized in that, The adjustment of the weights corresponding to the estimated travel time, the real-time load index, the emergency response capacity deviation item, and the number of available beds based on the severity of the illness includes: If the severity of the illness is Level 1, then the weight of the estimated travel time is greater than the weight of any one of the real-time load index, the emergency response capability deviation item, and the number of available bed resources. If the disease severity level is Level 2, then the weight of the estimated travel time is the same as the weight of the real-time load index, and the weights of the emergency response capability deviation item and the number of available bed resources are both less than the weight of the estimated travel time. If the severity of the illness is level three, then the weight of the estimated travel time is less than the weight of the real-time load index, and the sum of the weights of the emergency response capability deviation item and the number of available bed resources is the same as the weight of the real-time load index.

7. The method according to claim 1, characterized in that, The step of determining the target hospital from the set of candidate hospitals based on the comprehensive score of each candidate hospital includes: From the comprehensive scores of each candidate hospital, select the one with the lowest comprehensive score; The candidate hospital with the lowest comprehensive score is selected as the target hospital.

8. The method according to claim 1, characterized in that, The step of determining the severity of the target user's illness in response to the detection of an abnormal event of the target user includes: In response to the identification of the abnormal event of the target user, determine the corresponding multiple physiological data; Each physiological data point is compared with a preset disease threshold data; Based on the comparison results, the disease level of the target user is determined.

9. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause a computer to perform the method as described in any one of claims 1 to 8.

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