Time management optimization method and display device for ambulance

Abstract

The invention discloses a time management optimization method and a display device for an ambulance, and belongs to the technical field of medical first aid management. The method comprises the steps of obtaining an exclusive data set and multi-dimensional dynamic data of a target patient in real time, calling a preset demand rule base, calculating the emergency degree of the target patient in combination with the exclusive data set, and performing emergency level division; constructing a transfer planning model, screening a resource-adaptive target hospital for a target patient, synchronously planning a global optimal driving path, calculating predicted arrival time, and generating an individual transfer plan; global summarization is carried out on target patients to be transferred to form a matrix to be transferred, resource adaptation verification is carried out on the basis of a target hospital, an actual target transfer set is generated, and the transfer process is monitored in real time to update the predicted arrival time in real time; and after the transfer is completed, obtaining the actual arrival time, calculating the predicted deviation value, marking the deviation reason, forming an optimized data set, shortening the transfer time efficiency, and reducing the treatment delay risk.

Description

Technical Field

[0001] This invention relates to a time management optimization method and display device for ambulances, belonging to the field of medical emergency management technology. Background Technology

[0002] With the rapid development of network technology and the popularization of computer informatization and intelligence, the medical and health field is gradually entering the stage of digital transformation. As the core link of the emergency medical system, the level of information management of emergency centers directly affects the speed of emergency response and treatment efficiency. By integrating multi-dimensional information such as geographic information, communication data, and medical resources, a digital transfer planning platform can be built to achieve rapid allocation of emergency resources, scientific planning of emergency routes, and efficient linkage of cross-institutional collaboration, thereby promoting the transformation of emergency services from traditional experience-based to data-driven.

[0003] A Chinese patent with authorization announcement number CN113990464B discloses a planning method and system for urban emergency medical facilities. The planning method includes: obtaining the geographical locations of pre-hospital emergency facilities and in-hospital emergency facilities in a selected city, and establishing a pre-hospital-in-hospital emergency facility bipartite network, and obtaining the pre-hospital and in-hospital emergency facility networks respectively; obtaining the geographical locations of secondary and tertiary in-hospital emergency facilities in the selected city, and establishing a tiered in-hospital emergency facility network; obtaining the final in-hospital emergency facility network based on the in-hospital emergency facility network and the tiered in-hospital emergency facility network; and planning the emergency medical facilities in the selected city based on the pre-hospital emergency facility network and the final in-hospital emergency facility network.

[0004] While existing technologies have improved the efficiency of emergency medical care by planning urban emergency medical facilities, they have not considered the real-time dynamic adaptation of the planned reservation of hospital emergency resources to their actual occupancy status in multi-patient scenarios. In particular, they lack resource coordination mechanisms that fit the actual emergency response pace. For example, ICU beds and dedicated emergency equipment marked as vacant during planning may be temporarily occupied before patients arrive because the transfer time is not fully aligned with the sudden emergency needs within the hospital. Furthermore, when multiple patients have overlapping needs, it is difficult to quickly match alternative resources and make efficient adjustments according to the actual emergency response process. This results in some patients' actual emergency response time far exceeding the planned time, leading to a decline in overall emergency response efficiency. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide an optimization method and display device for time management of ambulances. This method quantifies the urgency of patients through objective indicators and dynamically calibrates weights. It matches hospital resources according to the correlation between urgency and resources, plans the globally optimal route, resolves the problems of resource mismatch and route inefficiency, calculates the estimated arrival time by combining multiple factors and updates it in real time to reduce time deviation, and solves the technical problem of lack of continuous improvement by relying on the deviation analysis iterative model.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Methods for optimizing time management for ambulances include:

[0008] The system acquires the target patient's exclusive dataset and multidimensional dynamic data in real time, calls the preset requirement rule base, calculates the urgency of the target patient in combination with the exclusive dataset, and classifies the urgency level.

[0009] A transfer planning model is constructed to select target hospitals with suitable resources for the target patients, simultaneously plan the globally optimal travel route, calculate the estimated arrival time, and generate individual transfer plans.

[0010] For the target patients to be transferred, a global summary is performed to form a multi-objective planning matrix. Based on the target hospital, resource adaptation verification is performed to generate the actual target transfer set. The transfer process is monitored in real time to update the estimated arrival time in real time.

[0011] After the transfer is completed, the actual arrival time is obtained, the expected deviation value is calculated, and the reasons for the deviation are marked to form an optimized dataset.

[0012] Specifically, the steps for classifying emergency levels include:

[0013] Using the degree of physiological abnormality, trauma level, and disease time sensitivity as judgment indicators, the standard parameter table of the judgment indicators is obtained by calling the aforementioned requirement rule base;

[0014] The relevant fields of the judgment indicators are extracted from the dedicated dataset, and after unified encapsulation, a basic dataset is generated.

[0015] Based on the aforementioned basic dataset, the real-time physiological parameters of the target patient are obtained, and the abnormality level of each physiological parameter is determined by combining the aforementioned standard parameter table.

[0016] Abnormal values ​​are assigned to various physiological parameters to obtain the degree of abnormality, and the abnormal physiological values ​​of the target patient are calculated by combining the weights.

[0017] Simultaneously acquire the trauma level and sensitivity of the target patient, and generate an emergency quantification table by combining the basic weights of the judgment indicators.

[0018] Specifically, the steps for classifying emergency levels also include:

[0019] Based on the historical emergency database, a real-time calibration coefficient is calculated, and the scenario adaptation coefficient is synchronously invoked to calibrate the sensitivity.

[0020] The system retrieves judgment data from the historical emergency database, calculates the matching deviation coefficient of each judgment indicator, corrects the basic weights, performs normalization processing, obtains the dynamic weights of each judgment indicator, and calculates the urgency of the target patient.

[0021] Set treatment target values ​​and obtain historical correlation data for different emergency levels, where emergency levels include... , , ;

[0022] For each emergency level, the minimum urgency required to achieve the treatment success rate target value is obtained, so as to set the emergency classification threshold, including the first-level classification threshold and the second-level classification threshold;

[0023] Based on the urgency level and urgency classification threshold of the target patient, the urgency level of the target patient is determined, and an urgency assessment report for the target patient is generated.

[0024] Specifically, the steps for generating an individual transit plan include:

[0025] Obtain strong constraints, and combine the emergency judgment report with the multidimensional dynamic data to generate a planning dataset;

[0026] Based on the target patients, the screening criteria are set to perform a preliminary screening of the candidate hospitals in the planning dataset to meet the resource standards, and a list of qualified hospitals is generated.

[0027] Obtain the available resources of qualified hospitals, calculate the resource matching degree, and simultaneously calculate the efficiency matching rate. Combined with the aforementioned strong constraints, calculate the demand matching rate.

[0028] The matching weight of the target patient is called, the fit score of the qualified hospital is calculated, and the hospital with the highest and second highest fit score is defined as the primary hospital and the alternative hospital, respectively, and a target hospital fit table is generated.

[0029] Obtain the path planning parameters and path constraints, and use A* path planning to generate multiple candidate paths.

[0030] Specifically, the steps for generating an individual transit plan also include:

[0031] Obtain the treatment time window for the target patient, calculate the time compliance, and simultaneously calculate the safety redundancy;

[0032] Combining the time compliance and the safety redundancy, the optimality score of each candidate path is calculated, the globally optimal driving path is selected, and a transfer planning table is generated.

[0033] The road condition fluctuation coefficient of the driving route is obtained, and the dynamic reserve time is calculated. Based on the emergency level of the target patient, the handover time is obtained. Combined with the estimated driving time in the transfer plan table, the target driving time is calculated, thereby generating the estimated arrival time.

[0034] Once the target travel time exceeds the rescue time window, an emergency adjustment mechanism is triggered;

[0035] Develop preliminary target plans, perform resource usage and route verification, and generate individual transfer plans.

[0036] Specifically, the steps for global aggregation include:

[0037] Batch retrieve all individual transit plans and generate a multi-objective planning matrix;

[0038] The multi-objective planning matrix is ​​classified according to the primary selected hospital to generate a target hospital transfer matrix, and the resource lock status is pending confirmation.

[0039] Once there are multiple [various types] in the matrix to be transferred For patients at the first level, perform resource adaptation verification on the target hospital, update the resource locking status to locked, and generate the actual target transfer set;

[0040] During the transfer process, the status of locked resources is updated in real time, and the urgency of the target patient is calculated.

[0041] Once the emergency level of the target patient is upgraded, the resource escalation process will be initiated immediately.

[0042] Specifically, the steps for resource compatibility verification include:

[0043] Obtain each of the elements in the matrix to be transferred. The resource needs of patients at all levels are analyzed and grouped by resource type to generate a target resource set. Simultaneously, a set of available resources for the target hospital is generated. The target resource set is compared with the same type of resources in the set of available resources to determine whether the resources are suitable.

[0044] If the number of available resources of all types is greater than the target number, then the resource is considered to be compatible and the resources are locked.

[0045] Otherwise, it is determined that the resources do not match, the insufficient resources are filtered out, and it is determined whether there is a unique match;

[0046] If it has a unique match, then for For patients in the first-level category, a rapid switchover process is triggered.

[0047] If there is no unique match, integrate the corresponding insufficient resources. Patients at level 1 were ranked based on both urgency and estimated arrival time, with those at the top of the list. Level 1 patients have their resources locked, while other unlocked resources are... For patients at level 1, a tiered screening process will be implemented using alternative hospitals.

[0048] Specifically, the steps for calculating the expected deviation value include:

[0049] Construct a transport aggregation table, obtain the actual arrival time of the target patients, calculate the expected relative deviation, and for target patients who require conflict adjustment, calculate the adjusted relative deviation.

[0050] A two-level classification threshold is set for the relative deviation of each emergency level, the relative deviation is classified, and the deviation level is marked.

[0051] Obtain the deviation amount for each deviation level, calculate the distribution ratio of each deviation level under different emergency levels, filter the types of deviation concentration, and construct a deviation statistics table;

[0052] A three-tiered cause classification system was established to match causes for severe deviation cases. For deviation clustering types, the fishbone diagram analysis method was used to locate the root cause.

[0053] By integrating data with severe biases and bias set types, an optimized dataset is generated.

[0054] The time management display device for ambulances includes: an emergency data collaboration terminal, a data processing center, and a time display.

[0055] The emergency data collaboration terminal is used to acquire the target patient's exclusive dataset and multi-dimensional dynamic data in real time.

[0056] The transfer computing power center is used to calculate the urgency of the target patient based on the judgment index, determine the urgency level of the target patient, calculate the estimated arrival time and perform global summary, construct a multi-objective planning matrix, and generate a transfer matrix based on the target hospital to perform resource adaptation verification.

[0057] The time display is used to synchronously receive and display the estimated arrival time of the power transfer center.

[0058] Specifically, the computing power center includes a judgment module, a planning module, a verification and scheduling module, and a deviation analysis module;

[0059] The judgment module is used to quantify judgment indicators, calculate the urgency of the target patient by combining weight allocation, classify the urgency level, and generate an emergency judgment report.

[0060] The planning module is used to construct a planning dataset, perform hospital screening and route planning, calculate and verify the estimated arrival time, and generate individual transfer plans.

[0061] The verification and scheduling module is used for global aggregation, constructing a transfer matrix based on the target hospital, and handling multiple... The system performs resource adaptation verification on the transfer matrix of patients at the primary level, generates the actual target transfer set, and monitors the transfer process in real time.

[0062] The deviation analysis module is used to calculate and classify the relative deviation after the transfer is completed, and generate an optimized dataset.

[0063] The beneficial effects of this invention are:

[0064] By quantifying urgency using objective indicators and combining dynamic weight calibration to avoid subjective bias, high-urgency needs are prioritized for identification, providing accurate decision-making basis for subsequent resource allocation. This mitigates the risk of underestimating high-urgency patients from the outset. Individual transport planning incorporates strong constraints from family members, selecting hospitals that balance resource availability with family demands. Route planning generates multiple candidate solutions and verifies them against treatment time windows. Estimated arrival times are calculated based on actual operational sequence and simultaneously communicated to families. This ensures planning aligns with needs and safety, reduces disputes caused by information asymmetry, and increases family trust. Furthermore, resource availability verification avoids false resource idleness. The system dynamically locks out expired resources at different levels to reduce waste, handles insufficient resources according to different scenarios, quickly switches between single patients, and prioritizes multiple patients based on a two-dimensional ranking system, resolving resource mismatch when multiple patients are concurrent and ensuring that highly urgent patients receive resources first. During transport, resource and patient status are monitored in real time, and routes and hospitals are quickly adjusted in case of sudden resource shortages or escalation of emergency levels to avoid delays. Finally, through deviation analysis and iterative modeling, the accuracy of judgment and planning is continuously optimized, shortening overall transport time, reducing the risk of treatment delays, and significantly improving emergency medical coordination efficiency and patient treatment success rates. Attached Figure Description

[0065] Figure 1 A schematic diagram illustrating methods for optimizing time management for ambulances;

[0066] Figure 2 This is a flowchart illustrating the emergency level classification in this invention;

[0067] Figure 3 This is a flowchart for generating individual transport plans in this invention;

[0068] Figure 4 A structural diagram of a time management display device for ambulances. Detailed Implementation

[0069] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0070] Example 1

[0071] refer to Figures 1 to 3 As shown in the figure, this embodiment introduces a time management optimization method for ambulances, including the following steps:

[0072] Step S1: For each target patient to be transferred, through multi-terminal collaboration and multi-platform integration, obtain the exclusive dataset and multi-dimensional dynamic data of each target patient in real time;

[0073] The system automatically collects the target patient's actual physiological parameters, such as heart rate, blood oxygen saturation, blood pressure, and trauma level, through vehicle-mounted terminals, such as multi-parameter monitors and portable ultrasound devices. Emergency personnel then input the initial disease type (e.g., acute myocardial infarction, stroke, trauma) using standardized electronic forms. After preprocessing by edge computing nodes, a personalized dataset for the target patient is generated. This multi-dimensional dynamic data includes vehicle dispatch data, dynamic environmental data, and related medical data. Utilizing BeiDou / GPS dual-mode positioning, vehicle-mounted sensors, and 5G real-time communication technology, vehicle dispatch data is collected, including the target vehicle's real-time latitude and longitude location, instantaneous speed, and engine status. The system monitors operational status and current task availability by connecting to the city's traffic big data platform API and meteorological department's real-time data interface to obtain dynamic environmental information, including real-time urban road conditions, temporary traffic control information, weather influencing factors, and future road condition predictions generated by traffic prediction models. Through standardized interfaces for hospital information and intensive care unit management, it synchronizes in-hospital emergency facility resource load data in real time, thereby obtaining related medical resources, including the number of available treatment units in the emergency department, available ICU beds, operating room occupancy status, availability of key emergency equipment, and the professional qualifications of on-duty medical staff, ensuring the accuracy of resource matching.

[0074] Step S2: Call the preset demand rule base, combine it with the target patient's exclusive dataset, use the degree of abnormality of physiological parameters, trauma level, and disease time sensitivity as judgment indicators, quantify and score each judgment indicator, summarize according to the set weight ratio, calculate the urgency of the target patient, and classify the urgency level. Dynamically match the target patient through the constructed transfer planning model, combine the target patient's urgency level with the real-time resource status of the in-hospital emergency facilities, select the target hospital with suitable resources for the target patient, and ensure that the hospital's facilities, personnel configuration and treatment needs match the target patient's treatment needs. Combine dynamic environmental information to plan the global optimal driving route, taking into account driving efficiency and route safety. Based on the length of the driving route, the expected driving speed and stop nodes, calculate the expected arrival time of the target patient's transfer, thereby generating an individual transfer plan for the target patient, ensuring that the planning results meet the patient's treatment needs and are adapted to the current resource and environmental conditions.

[0075] Step S3: Based on the target patients to be transferred, all individual transfer plans are aggregated to the cloud dispatch center for global aggregation, forming a multi-objective planning matrix. This matrix includes the target hospital, resource type, and estimated arrival time for each target patient. The primary selected hospital is used as the target hospital. The multi-objective planning matrix is ​​classified based on the target hospitals to generate a transfer matrix for each target hospital. Resource adaptation verification is performed on the transfer matrix to generate the actual target transfer set. Real-time monitoring is carried out during the multi-objective transfer process. The exclusive dataset and multi-dimensional dynamic data of the target patients are continuously acquired, and the urgency of the target patients is updated in real time to update the estimated arrival time in real time, ensuring that the estimated arrival time is consistent with the actual transfer situation. The update results are synchronized to the dispatch terminal, ambulance, and target hospital.

[0076] Step S4: After the ambulance completes the transfer task, the actual arrival time of the ambulance is automatically obtained. At the same time, the estimated arrival time records during the transfer process are retrieved, and the estimated deviation value is calculated. Based on the real-time monitoring logs during the transfer process, the specific reasons for the deviation are marked. The patient-specific dataset, the estimated arrival time at each stage, the actual arrival time, the deviation value, and the reasons for the deviation are organized in a unified format to form a structured optimized dataset. The urgency determination logic of the demand rule base, the path planning algorithm of the transfer planning model, and the estimated arrival time calculation logic are periodically iterated and optimized to continuously improve the accuracy of urgency determination, the rationality of planning, and the accuracy of estimated arrival time prediction in multi-patient concurrent scenarios, thereby achieving a closed-loop improvement in emergency time management capabilities.

[0077] Specifically, the steps for classifying emergency levels include:

[0078] The degree of physiological abnormality, trauma level, and disease time sensitivity are defined as judgment indicators. The requirement rule library is called to obtain the standard parameter table of the judgment indicators, including the physiological abnormality level table, the trauma quantification table, and the sensitivity initial table. The physiological abnormality level table is formulated according to the national clinical emergency guidelines, which clarifies the normal range of different physiological parameters and the basis for classifying mild / moderate / severe abnormalities. The trauma quantification table converts the trauma level into a value to be calculated. The sensitivity initial table assigns an initial score to each disease according to the golden treatment time requirements of different diseases. The completeness of the standard parameter table is checked. If there are missing parameters, such as the severe abnormality threshold of a certain physiological parameter not being recorded, the off-site backup parameter library is automatically called and reloaded until the parameters are complete, avoiding calculation interruption due to incomplete parameters and ensuring that all calculations have clear and unified standard basis.

[0079] The relevant fields of the judgment indicators are extracted from the exclusive dataset of the target patients, including the original records of the patients' physiological parameters, descriptions of trauma, and disease type codes. The extracted information is then packaged in a unified format preset by the engine to ensure that the data can be read smoothly by the engine and to avoid calculation deviations caused by format incompatibility, thereby generating the basic dataset.

[0080] Based on a basic dataset, real-time physiological parameters of the target patient, such as heart rate, blood oxygen saturation, and systolic blood pressure, are obtained. Using a physiological abnormality grading table, the abnormality level of each physiological parameter is determined, including severe, moderate, mild, and no abnormality. Since each abnormality level is assigned a different value, the abnormality level is used to assign abnormal values ​​to each physiological parameter, resulting in the degree of abnormality for each parameter. Furthermore, based on the degree of impact of each physiological parameter on life safety, weights are assigned to each parameter; for example, blood oxygen saturation, which is directly related to the risk of hypoxia, has a higher weight. The physiological abnormality values ​​of the target patient are then calculated. This is to quantify the degree of physiological abnormality and intuitively reflect the urgency of the patient's physiological state;

[0081] Trauma level descriptions are extracted from the basic dataset and directly matched with the trauma quantification table in a one-to-one correspondence between descriptions and values ​​to obtain the corresponding trauma level and degree of trauma. Simultaneously, based on the disease type code, the sensitivity corresponding to the disease is matched from the initial sensitivity table. For diseases with higher time sensitivity, such as acute myocardial infarction, the initial score is higher, reflecting the urgency of the need for rapid transfer. Combining the basic weights of the judgment indicators, an emergency quantification table is generated, which records the quantification value and calculation basis of each judgment indicator in detail.

[0082] Because emergency scenarios are dynamic, such as the high incidence of acute myocardial infarction in winter or sudden trauma events in a certain area, fixed dimensional weights cannot adapt to these changes, which can easily lead to the underestimation of patients with high emergency needs. By combining historical data with the current scenario, the weights of the three judgment indicators and the initial scores of disease time sensitivity are dynamically adjusted. The success rate of treatment for the target patient's disease under different time sensitivity weights is obtained from the historical emergency database. Based on the historical treatment effect of the current month, the corresponding scenario adaptation coefficient is called. At the same time, the monthly incidence rate and annual average incidence rate of the disease are obtained. The real-time calibration coefficient is calculated by the ratio of the monthly incidence rate and the annual average incidence rate. The calibrated sensitivity is obtained by multiplying the initial sensitivity, the scenario adaptation coefficient and the real-time calibration coefficient. The scenario adaptation coefficient is preset by those skilled in the art based on historical seasonal incidence patterns.

[0083] The system retrieves judgment data from the historical emergency database for the past three months, including the matching rate between the judgment results of each judgment indicator and the actual treatment priority. It calculates the average matching rate of all judgment indicators, and combines the matching rate of individual judgment indicators with the matching deviation calculation formula to calculate the matching deviation coefficient of each judgment indicator. The system then adjusts the basic weight of each judgment indicator by adding the basic weight to the product of the matching deviation coefficient and the basic weight to obtain the calibration weight of each judgment indicator. The calibration weight is then normalized to obtain the dynamic weight of each judgment indicator, ensuring that the sum of the weights of the three judgment indicators is 1. This avoids calculation logic confusion caused by the deviation of the total weight. The dynamic weight is then saved to the emergency quantification table for updating the emergency quantification table.

[0084] Based on the emergency quantification table, the quantified values ​​of the three judgment indicators are combined with dynamic weights, and the urgency of the target patient is calculated by weighted summation. The urgency is then formatted with precision to ensure that the urgency of different patients is comparable under the same precision standard. At the same time, an emergency target result sheet is generated, which records in detail the quantified value, weight and urgency of each judgment indicator and the target patient.

[0085] Historical correlation data for different emergency levels over the past 12 months was retrieved, including the treatment success rate of patients at different emergency levels under different estimated arrival times and the resource utilization of patients at each level. Emergency levels include... , , Based on regional emergency medical care quality standards, treatment target values ​​are set as the minimum success rate standards for different emergency levels. Since the emergency levels here include three levels, the treatment target values ​​include Level 1, Level 2, and Level 3 target values, such as... The target rate for treatment at the primary level is 92%. The target value for treatment at the primary level is 85%. The target treatment rate for Level 1 emergency care is 80%. Historical treatment data, including correlation data between emergency scores and treatment success rates at different levels of urgency, is retrieved to generate a scatter distribution, such as the distribution of different levels of urgency. The success rate of treatment for patients at each emergency level is used to deduce the total score range for each emergency level by working backward from the treatment target value. For patients at level 1, all those with a treatment success rate exceeding the corresponding treatment target value were selected from the scatter distribution. Patients were classified as level 1, and an urgency score was extracted, with the lowest urgency score being used as the baseline. Level and The threshold for class division, for For patients in the first urgency level, the lowest urgency value is also used as the threshold. Level and The threshold for classifying patients into different levels is used to derive the total score range based on the threshold, ensuring that limited high-level resources are prioritized for the most urgent patients, achieving precise matching of resources and needs, while ensuring that the success rate of treatment for patients at all levels meets the standards.

[0086] Based on the urgency level and urgency classification threshold of the target patient, the corresponding urgency level is determined, and an urgency assessment report is generated. This report includes the target patient's basic information, urgency level, urgency grade, quantitative values ​​of each assessment indicator, and the corresponding treatment target value and resource allocation standard for each urgency level. Specifically, resource allocation standards for each urgency level are set based on resource usage data. On average, each critically ill patient occupies one ICU bed. The resource utilization standard is to match ICU resources.

[0087] Specifically, the steps for generating an individual transit plan include:

[0088] From the emergency assessment report, decision-making information determining the planning direction is extracted, including the target patient's emergency level, resource allocation standards, and treatment target values. Simultaneously, planning-related data is extracted from multi-dimensional dynamic data, and combined with the family's explicit demands entered through the vehicle terminal, these are marked as strong constraints. Furthermore, based on the preliminary disease type, it is determined whether there is a need for specialized treatment; if so, this need is marked as [missing information]. Strong constraints on grade 1 patients, non- The system flexibly adapts to different patient levels, generating a planning dataset for the target patient. This dataset includes data across four dimensions: patient, vehicle, hospital, and environment. The patient dimension includes the target patient's real-time location, emergency level, resource occupancy standards, and treatment target values. The target patient's real-time location is obtained from vehicle dispatch data. Since there is a one-to-one correspondence between ambulances and target patients, the real-time location of the ambulance also represents the real-time location of the target patient. The vehicle dimension includes basic ambulance information for the target patient. The hospital dimension includes the real-time resource status, geographical location, and specialty qualification tags of all candidate hospitals. The environment dimension includes real-time traffic conditions from the target patient's location to each candidate hospital, future traffic condition predictions, temporary traffic control areas, and ambulance emergency passage route markers.

[0089] Using the resource occupancy standards and specialist needs in the emergency assessment report generated for the target patient as screening criteria, the candidate hospitals in the planning dataset are initially screened for resource adequacy. If the target patient is... For patients classified as Level 1 or above, only hospitals with corresponding proprietary resources that are not locked by patients of the same level will be retained, such as PCI equipment and isolation ICUs. If the target patient is... class, For patients with high-risk conditions, hospitals with basic resources, such as emergency departments and general beds, are selected. At the same time, strong constraints from families are taken into account, such as removing hospital types that families refuse or hospitals that do not meet the resource requirements. A list of qualified hospitals for the target patients is generated, and the remaining resources of qualified hospitals are recorded to avoid wasting computational resources on ineffective hospitals. This ensures that the selected hospitals meet the core treatment needs of patients.

[0090] Although hospitals that have passed the initial screening for resource compliance meet the resource requirements, there are still differences in treatment capacity, distance, and resource sufficiency. The resource matching degree is calculated based on the ratio of the current available resources of the qualified hospitals to the resources required by patients. The efficiency matching degree is calculated based on the reciprocal of the product of the straight distance from the target patient's location to the qualified hospital and the road condition influence coefficient. At the same time, combined with strong constraints, the total number of constraints and the number of constraints that qualified hospitals meet are obtained. The demand matching degree is calculated based on the ratio of the number of constraints to the total number.

[0091] Based on the urgency level of the target patient, the corresponding matching weights are applied, and the suitability score of each qualified hospital in the list is calculated through weighted summation. Where, if For patients with high-risk conditions requiring specialized treatment, the weight of the matching degree is recalculated based on the specialized enhancement coefficient, and the weight is normalized to obtain an updated suitability score. The qualified hospitals are then sorted in descending order according to their corresponding suitability scores. The hospital with the highest suitability score is defined as the primary hospital, and a resource reservation request is simultaneously sent to the hospital to obtain a temporary lock-in certificate. The hospital with the second highest suitability score is defined as the backup hospital to avoid the lack of alternative options due to sudden changes in the primary hospital's resources. A target hospital suitability table is generated, including the primary / backup hospital name, geographical location, resource matching details, suitability score, and screening criteria, and key communication points for family members are simultaneously marked. The matching weights are set by those skilled in the art.

[0092] Based on the planning dataset and the target hospital adaptation table, route planning parameters are obtained, including the start point, destination, ambulance parameters, and road condition data. Furthermore, based on the emergency level of the target patient, route constraints are derived, such as… Patients with severe congestion should avoid road sections with a congestion index greater than the first level, prioritize routes with emergency lanes, and not be bound by strict adherence to general traffic rules. This is to avoid missing the optimal treatment window by strictly following ordinary traffic rules, and to seize the golden opportunity for treatment. class, Patients with severe congestion should avoid road sections with a congestion index greater than the second congestion coefficient. Clear boundary conditions should be set for route planning to avoid planning routes that do not meet the capacity or urgency requirements of ambulances. The starting point is the real-time location of the target patient, and the destination is the geographical location of the primary hospital.

[0093] Using the A* path planning algorithm, three candidate paths with different orientations are generated: shortest distance priority, shortest time priority, and dynamic safety priority. Each candidate path is labeled with its estimated travel time, key road segments, and congestion risk warnings. Among these, [the following is a list of paths, likely related to path planning]. For patients with severe symptoms, an optimized emergency passage route is added, and the points where traffic rules need to be broken are marked. Traffic police are notified simultaneously to ensure that the fastest route that can flexibly break the rules is selected, avoiding the planning of compliant but time-consuming invalid routes based on conventional vehicle logic.

[0094] Based on the estimated treatment time database, the treatment time window corresponding to the target patient is called. The time compliance is calculated based on the ratio of the estimated travel time to the length of the treatment time window, and the safety redundancy is calculated. Candidate routes containing emergency lanes are assigned a first redundancy score, and routes passing through hospitals are assigned a second redundancy score. Through weighted calculation, each candidate route is scored, and the optimality score is calculated. The route with the smallest optimality score is taken as the global optimal travel route, and a transfer planning table is generated, which includes the route start / end point, details of the road segments, estimated travel time of each road segment, congestion risk level, whether it contains an emergency lane, total route length, and estimated travel time.

[0095] Based on the estimated travel time in the transfer plan, a dynamic reserve time is introduced to cope with sudden changes in road conditions. The road condition fluctuation coefficient is calculated based on the congestion index of the travel route, and the dynamic reserve time is calculated in combination with the estimated travel time. At the same time, based on the emergency level of the target patient, the handover time of the target patient from the ambulance to the in-hospital emergency unit is obtained. The target travel time is calculated based on the estimated travel time and the dynamic reserve time. Combined with the current time, the estimated arrival time is calculated. The data is displayed simultaneously on the in-vehicle display in a split screen. In the medical staff view, only the estimated arrival time and the in-hospital contact person are displayed, so that doctors can focus on patient treatment without being distracted by the route progress. In the family view, the data is displayed in the form of a countdown and emergency passage instructions. The clear time feedback alleviates the anxiety of the family and reduces the interference of frequent inquiries from the family to the medical staff.

[0096] The system uses the treatment time window to verify whether the target travel time is within the treatment time window. If the target travel time is within the treatment time window, the target travel time is retained. If the target travel time exceeds the treatment time window, the emergency adjustment mechanism is triggered, and the hospital selection and route planning are re-performed. In the route planning, the emergency lane is activated and the route constraints are corrected until the target travel time is within the treatment time window. The reason for the adjustment is recorded simultaneously.

[0097] The target hospital matching table, transfer plan and estimated arrival time are integrated to generate a preliminary target plan and perform feasibility verification. A primary hospital resource lock request is sent to the interface that obtains related medical data to confirm whether the planned hospital resources are still available within the estimated arrival time period to avoid other systems occupying them at the same time. Once the resources are occupied, the backup hospital is immediately activated, the route planning and estimated arrival time are recalculated, and the plan content is updated. At the same time, the system is connected to the city's real-time traffic event monitoring platform to confirm whether there are any new traffic controls or sudden major accidents on the optimal route. If so, the candidate route is called, the second-best route is selected and the estimated arrival time is recalculated to ensure that the route still meets the urgency requirements.

[0098] After verification, an individual transport plan for the target patient is generated and packaged in a unified standard format. This plan includes the target patient identifier, emergency level, transport plan table, primary / alternate hospitals, resource requirement type, target travel time, estimated arrival time, and plan generation time. The plan is then synchronized with the dispatch center, ambulance onboard terminal, and target hospital information system. This ensures information sharing and collaboration among the dispatch center, ambulance, and hospital. The dispatcher knows the plan for each patient, medical staff know the route and arrival time, and the hospital can prepare resources in advance, preventing inefficiencies caused by information asymmetry.

[0099] Specifically, the steps for resource compatibility verification include:

[0100] Through a standardized interface, all individual transport plans generated after ambulances arrive at the scene are obtained in batches and linked to the emergency assessment reports of each patient to be transported. The plans are then bound by the patient's unique identifier to ensure that each individual transport plan has a clear basis for urgency, thus avoiding the situation where plans without priority compete for resources. Finally, a multi-objective planning matrix is ​​generated.

[0101] The multi-objective programming matrix is ​​categorized according to the primary hospital to generate a transfer matrix for the target hospital, including hospital name, patient identifier, emergency level, resource requirements, estimated arrival time, and resource lock-in status. At this point, the resource lock-in status is "pending confirmation." Patients with high-risk conditions are individually marked with priority for their resource needs, which facilitates accurate matching of hospital resource types in the future.

[0102] For each target hospital, the system connects to its information system to retrieve real-time status of target resources, such as the number of available ICU beds, the availability of specialized equipment, and the on-duty status of medical staff in the corresponding departments. Simultaneously, it automatically dials the hospital's emergency department hotline via a pre-set extension number for manual verification of resource availability, avoiding false vacancy caused by system data delays. After confirmation, a locking interval is set to... The resource lock validity period is dynamically set for tiered patients. Resources are marked as reserved within the validity period, and hospitals must retain these resources within that period. If a critically ill patient does not arrive, resources will be automatically released and the hospital will be notified to avoid prolonged resource occupation.

[0103] When there are multiple in the transfer matrix For patients at the first level, the resources of the target hospital are verified, and each item in the transfer matrix is ​​obtained. The resource needs of patients at all levels are analyzed and grouped by resource type to generate a target resource set. Simultaneously, the available resources of the target hospital are obtained for each type of resource in the target resource set to generate a set of available resources for the target hospital.

[0104] The target resource set is compared with the similar resources in the idle resource set in turn to determine whether the target hospital and the transfer matrix are resource-matched.

[0105] If the number of available resources of all resource types in the available resource set is greater than the target number in the target resource set, it indicates that the target hospital's available resources satisfy all... For patients with high-risk conditions, the resource needs of the target hospital and the resources in the transfer matrix are matched, and at the same time, for all... For patients at the first level, resources are locked, and the resource lock status in the transfer matrix is ​​updated to "locked". No planning adjustment is required. At the same time, a notification that the resources are locked is pushed to the ambulance terminal, along with navigation guidance to the hospital emergency entrance.

[0106] Otherwise, it is determined that the resources do not match, and the resource types in the idle resource set whose resource quantity is not greater than that in the target resource set are selected and marked as insufficient resources;

[0107] The insufficient resources are processed according to different scenarios to determine whether the insufficient resource has unique matching, that is, whether the insufficient resource is only one Resource needs of patients at the primary level;

[0108] If there is a unique match, then for the corresponding For patients in the first-level category, a rapid switching process is triggered, immediately retrieving the alternative hospitals from the individual transfer plan, reconfirming the resources in real time, immediately updating the primary hospital to the alternative hospital, and simultaneously replanning the optimal travel route and updating the estimated arrival time.

[0109] If there is no unique match, it means that there are not enough resources to correspond to multiple people. For patients at the primary level, integrate the corresponding insufficient resources Patients were ranked according to both urgency and estimated arrival time to avoid ignoring the time urgency factor by sorting solely by urgency. They were then sorted in descending order of urgency score, followed by ascending order of estimated arrival time, with the first-ranked patient being ranked first. For patients at the highest level, resources are locked; the resource lock status is updated to locked; the hospital is notified to reserve the resources. For the remaining unlocked resources... For patients with severe symptoms, a tiered screening of alternative hospitals is implemented, prioritizing alternative hospitals within 3 kilometers of the primary hospital that have available resources and the same treatment capabilities. If the primary hospital is a top-tier hospital, alternative hospitals are also prioritized. If there is no suitable hospital within 3 kilometers, the search is expanded to within 5 kilometers. Two alternative hospital plans are generated simultaneously for these patients to avoid sudden resource occupancy of a single alternative hospital. Each plan includes resource confirmation status, estimated arrival time, and route preview, allowing emergency personnel to quickly select the appropriate plan based on family opinions.

[0110] After all resource adaptation processing is completed, all adjusted planning data are integrated to generate the actual target transfer set, including resource lock status, primary hospital adjustment results, new estimated arrival time, alternative plan selection records, and a resource adaptation summary report is generated. The report is pushed to the dispatch terminal, hospital terminal, and ambulance terminal in real time to ensure that each link has a grasp of the final adaptation results.

[0111] During the transfer process, the real-time status of locked resources is automatically refreshed every preset time interval. If the hospital reports a sudden occupancy of locked resources, such as a reserved ICU bed being occupied by a critically ill patient in the hospital, emergency adaptation is immediately triggered. The backup hospital in the patient's individual transfer plan is prioritized, and the hospital is simultaneously notified to release the originally locked resources. The new route and estimated arrival time are recalculated and pushed to the ambulance terminal, the hospital emergency desk, and the family view. If the resource status is normal, the lock is maintained until the patient arrives at the hospital and the handover is completed. At this time, a resource usage confirmation is automatically sent to the hospital, and the lock is released.

[0112] Meanwhile, during the transfer, the vehicle's IoT terminal continuously collects the target patient's physiological parameters and calculates the patient's urgency in real time. If the patient's urgency level increases, such as from... Level upgraded to Level, original If the locked resources (such as general emergency room beds and basic monitoring equipment) can no longer meet the demand, immediately initiate the resource upgrade process, send a resource upgrade request to the original primary hospital, and release the original resources to avoid occupying low-level resources. Prioritize verifying whether the original primary hospital has such resources. If the primary resources are available, the original plan will proceed. If no resources are available, the primary hospitals will be reselected, prioritizing those within a 3-5 kilometer radius of the original primary hospitals. Newly identified hospitals with advanced treatment capabilities Level 1 resources must be marked as urgently needed, and hospitals must prioritize them above ordinary resources. Level 1 patients, such as those originally in the queue Patients with severe conditions will be postponed, and priority will be given to patients who have been upgraded this time. The system will simultaneously push a description of the patient's deterioration to the hospital as a basis for priority treatment, and will re-plan the route and update the estimated arrival time.

[0113] Specifically, the steps for calculating the expected deviation value include:

[0114] The relevant data of multi-target transportation are integrated to generate a transportation aggregation table, which includes transportation monitoring data, transportation planning data and transportation results;

[0115] Obtain the estimated arrival time in the individual transport plan, and simultaneously obtain the actual arrival time of the target patient. Calculate the estimated relative deviation based on the ratio of the absolute difference between the estimated and actual arrival times to the estimated arrival time. For target patients requiring conflict adjustment, calculate the adjusted relative deviation based on the final adjusted estimated arrival time.

[0116] Based on the urgency level, a deviation grading standard is established, and a secondary grading threshold is set for the relative deviation of each urgency level to classify the relative deviation index, clarifying the acceptable range of deviation for patients with different urgency levels, including acceptable deviation, general deviation, and severe deviation. The relative deviation of each target patient is compared with the grading threshold of the corresponding urgency level, and the deviation level is marked. Since the relative deviation includes the expected relative deviation and the adjusted relative deviation, the relative deviation before and after adjustment is divided separately when classifying the deviation level, and it is marked whether it is an adjusted deviation level. For target patients who have not undergone conflict adjustment, only one deviation level is included, while for target patients who have undergone conflict adjustment, an adjusted deviation level is also included.

[0117] Under different urgency levels, the deviation amount of each deviation level is obtained. Based on the number of target patients under the corresponding urgency level, the distribution ratio of each type of deviation level is calculated. Deviation levels with distribution ratios exceeding the distribution threshold are defined as deviation set types. A deviation statistics table is constructed, including target patient identification archives, relative deviations, and deviation levels. Distribution ratios are calculated for both expected relative deviations and adjusted relative deviations. In expected relative deviations, the number of all target patients is counted because all target patients have expected relative deviations. In adjusted relative deviations, only the number of target patients after conflict adjustment is counted.

[0118] Establish a three-tier cause classification system to ensure that cause classification can directly correspond to the technical links of the preceding process, including process classification, problem classification, and technical root cause classification, to avoid vague cause classification, and automatically match cause classification for serious deviation cases, generate a single patient deviation cause list, and mark the results of each level of classification and the associated preceding step log number to ensure traceability;

[0119] For the type of deviation concentration, the fishbone diagram analysis method is used to locate the root cause and generate a deviation root cause analysis report, which includes the root cause description of the high-frequency cause, the verification process, and the associated preceding step parameters.

[0120] By integrating data on severe deviations and deviation clusters, an optimized dataset is generated, covering case information, deviation data, cause data, and correlation parameters, to optimize hospital selection, route planning, and estimated arrival time calculation.

[0121] Example 2

[0122] Please see Figure 4 Another embodiment of the present invention provides a time management display device for ambulances, comprising: an emergency data collaboration terminal, a data processing center, and a time display;

[0123] The emergency data collaboration terminal is used to interface with multiple platforms to obtain real-time exclusive datasets and multi-dimensional dynamic data of patients awaiting transfer. Among them, it can automatically collect patients' physiological parameters by relying on relevant vehicle-mounted equipment and assist emergency personnel in entering preliminary disease types according to standards. After preprocessing by edge computing nodes, the data integrity and validity are ensured. At the same time, it connects with urban traffic, weather and hospital information to obtain vehicle dispatch, dynamic environment and related medical resource data, providing real-time and accurate basic data support for subsequent time management calculations, avoiding the impact of data lag or missing data on the accuracy of time planning.

[0124] The transfer processing center is used to determine the emergency level of the target patient, match the target hospital with multi-dimensional data, plan the optimal route, and calculate the estimated arrival time. A multi-objective planning matrix is ​​constructed through global aggregation, with the primary selected hospital as the target hospital. The multi-objective planning matrix is ​​classified based on the target hospitals to generate a transfer matrix for each target hospital. Resource adaptation verification is performed, and the transfer process is monitored in real time to update the estimated arrival time. After the transfer, the deviation value is calculated, the cause is analyzed, and the rules and algorithms are iteratively optimized to realize the dynamic adjustment and closed-loop improvement of emergency time management, thereby improving the rationality of planning and the accuracy of time prediction.

[0125] The computing power center includes a judgment module, a planning module, a verification and scheduling module, and a deviation analysis module.

[0126] The judgment module is used to call the preset demand rule library, combine it with the patient-specific dataset, and use the degree of abnormality of physiological parameters, trauma level, and disease time sensitivity as judgment indicators to perform quantitative scoring and dynamic weight adjustment, calculate the patient's urgency, determine the level classification threshold based on regional emergency care quality standards and historical treatment data, so as to classify the urgency level, and generate an emergency judgment report containing the patient's basic information, urgency level and resource consumption standards, to ensure that the urgency judgment is in line with the real-time scenario and treatment needs, provide accurate priority basis for subsequent target planning, and avoid underestimating patients with high urgency needs;

[0127] The planning module is used to extract decision-making information from emergency assessment reports and planning-related data from multi-dimensional dynamic data, generate planning datasets, screen qualified hospitals with suitable resources and calculate the suitability to determine primary and alternative hospitals, plan the globally optimal driving route based on dynamic environmental information, calculate the estimated arrival time based on handover time, in-hospital preparation time and dynamic reserve time, and verify whether the estimated arrival time meets the treatment time window and the feasibility of the plan. It generates individual transfer plans that include target hospitals, driving routes and estimated arrival times, ensuring that the planning results meet the patient's treatment needs and are in line with current resource and environmental conditions, avoiding ineffective planning and wasting time.

[0128] The verification and scheduling module is used to summarize all individual transfer plans to form a multi-objective planning matrix. Taking the primary selected hospital as the target hospital, the multi-objective planning matrix is ​​classified based on the target hospital, generating a transfer matrix for each target hospital. The module also performs resource adaptation verification on the transfer matrix to generate the actual target transfer set. During the multi-objective transfer process, the module performs real-time monitoring, continuously acquires the target patient's exclusive dataset and multi-dimensional dynamic data, updates the urgency of the target patient in real time, and updates the estimated arrival time in real time to ensure that the estimated arrival time is consistent with the actual transfer situation. The update results are synchronized to the dispatch terminal, ambulance, and target hospital.

[0129] The deviation analysis module is used to integrate transfer monitoring data, planning data and result data after the transfer is completed, calculate and classify the relative deviation between the estimated arrival time and the actual arrival time, analyze the causes of the deviation and locate the root cause, generate a structured optimization dataset, and iteratively optimize the urgency determination logic, route planning algorithm and arrival time calculation logic. It provides data support for iterative optimization of the urgency determination logic of the demand rule base, the route algorithm of the planning module and the estimated arrival time calculation logic, realize closed-loop improvement of emergency time management capabilities, and continuously improve the accuracy of time prediction.

[0130] The time display is used to synchronously receive individual transport planning, conflict scheduling results and deviation analysis data output by the transfer computing power center. It intuitively displays the patient's emergency level, target hospital, global optimal driving route, estimated arrival time, actual arrival time and deviation information, ensuring that the dispatcher, ambulance emergency personnel and target hospital obtain consistent key time and planning information in real time, avoiding inefficient collaboration due to information asymmetry, assisting all parties in accurately controlling the transport rhythm and improving multi-terminal collaboration efficiency.

[0131] Working principle and effects:

[0132] This approach integrates patient-specific physiological data with multidimensional dynamic data, constructing a quantitative model based on the degree of physiological parameter abnormalities, trauma severity, and disease-specific time sensitivity. It combines dynamic weight calibration to determine urgency, avoiding subjective human bias and ensuring accurate identification of high-urgency needs. This addresses the ambiguity in urgency assessment, providing a reliable basis for resource allocation. A transport planning model is built based on patient urgency and real-time hospital resources. When selecting suitable primary and secondary hospitals, strong constraints from family members are incorporated. Multiple candidate routes are generated and validated using treatment time windows. The estimated arrival time is calculated by integrating travel and handover times with dynamic allowances. This ensures accurate matching of resources and treatment needs while avoiding timeliness deviations caused by single factors, significantly improving planning rationality. Individual plans are aggregated into a matrix, and resource adaptation is validated according to urgency. The system sets dynamic resource lock-in validity periods for high-urgency patients to reduce waste, handles insufficient resources according to different scenarios, prioritizes resources and routes for high-urgency patients, and flexibly adjusts for low-urgency patients, effectively solving the problem of inefficient coordination when multiple patients are involved. During the transfer, the system monitors resource status and patient physiological parameters in real time. If resources are suddenly occupied or the patient's urgency level increases, emergency adjustments are immediately initiated and the dispatch terminal, ambulance, and hospital are synchronized to ensure accurate estimated arrival time. Finally, deviation analysis is used to locate the root cause, and the judgment logic and algorithm are iteratively optimized to continuously improve the accuracy of urgency, planning precision, and estimated arrival time reliability, thereby shortening the overall transfer time, reducing the risk of treatment delays, and significantly improving the efficiency of emergency coordination and the success rate of patient treatment.

[0133] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A time management optimization method for ambulances, characterized in that, include: The system acquires the target patient's exclusive dataset and multidimensional dynamic data in real time, calls the preset requirement rule base, calculates the urgency of the target patient in combination with the exclusive dataset, and classifies the urgency level. A transfer planning model is constructed to select target hospitals with suitable resources for the target patients, simultaneously plan the globally optimal travel route, calculate the estimated arrival time, and generate individual transfer plans. For the target patients to be transferred, a global summary is performed to form a multi-objective planning matrix. Based on the target hospital, resource adaptation verification is performed to generate the actual target transfer set. The transfer process is monitored in real time to update the estimated arrival time in real time. After the transfer is completed, the actual arrival time is obtained, the expected deviation value is calculated, and the reasons for the deviation are marked to form an optimized dataset.

2. The time management optimization method for ambulances according to claim 1, characterized in that, The steps for classifying emergency levels include: Using the degree of physiological abnormality, trauma level, and disease time sensitivity as judgment indicators, the standard parameter table of the judgment indicators is obtained by calling the aforementioned requirement rule base; The relevant fields of the judgment indicators are extracted from the dedicated dataset, and after unified encapsulation, a basic dataset is generated. Based on the aforementioned basic dataset, the real-time physiological parameters of the target patient are obtained, and the abnormality level of each physiological parameter is determined by combining the aforementioned standard parameter table. Abnormal values ​​are assigned to various physiological parameters to obtain the degree of abnormality, and the abnormal physiological values ​​of the target patient are calculated by combining the weights. Simultaneously acquire the trauma level and sensitivity of the target patient, and generate an emergency quantification table by combining the basic weights of the judgment indicators.

3. The time management optimization method for ambulances according to claim 2, characterized in that, The steps for classifying emergency levels also include: Based on the historical emergency database, a real-time calibration coefficient is calculated, and the scenario adaptation coefficient is synchronously invoked to calibrate the sensitivity. The system retrieves judgment data from the historical emergency database, calculates the matching deviation coefficient of each judgment indicator, corrects the basic weights, performs normalization processing, obtains the dynamic weights of each judgment indicator, and calculates the urgency of the target patient. Set treatment target values ​​and obtain historical correlation data for different emergency levels, where emergency levels include... , , ; For each emergency level, the minimum urgency required to achieve the treatment success rate target value is obtained, so as to set the emergency classification threshold, including the first-level classification threshold and the second-level classification threshold; Based on the urgency level and urgency classification threshold of the target patient, the urgency level of the target patient is determined, and an urgency assessment report for the target patient is generated.

4. The time management optimization method for ambulances according to claim 3, characterized in that, The steps to generate an individual transport plan include: Obtain strong constraints, and combine the emergency judgment report with the multidimensional dynamic data to generate a planning dataset; Based on the target patients, the screening criteria are set to perform a preliminary screening of the candidate hospitals in the planning dataset to meet the resource standards, and a list of qualified hospitals is generated. Obtain the available resources of qualified hospitals, calculate the resource matching degree, and simultaneously calculate the efficiency matching rate. Combined with the aforementioned strong constraints, calculate the demand matching rate. The matching weight of the target patient is called, the fit score of the qualified hospital is calculated, and the hospital with the highest and second highest fit score is defined as the primary hospital and the alternative hospital, respectively, and a target hospital fit table is generated. Obtain the path planning parameters and path constraints, and use A* path planning to generate multiple candidate paths.

5. The time management optimization method for ambulances according to claim 4, characterized in that, The steps involved in generating an individual transport plan also include: Obtain the treatment time window for the target patient, calculate the time compliance, and simultaneously calculate the safety redundancy; Combining the time compliance and the safety redundancy, the optimality score of each candidate path is calculated, the globally optimal driving path is selected, and a transfer planning table is generated. The road condition fluctuation coefficient of the driving route is obtained, and the dynamic reserve time is calculated. Based on the emergency level of the target patient, the handover time is obtained. Combined with the estimated driving time in the transfer plan table, the target driving time is calculated, thereby generating the estimated arrival time. Once the target travel time exceeds the rescue time window, an emergency adjustment mechanism is triggered; Develop preliminary target plans, perform resource usage and route verification, and generate individual transfer plans.

6. The time management optimization method for ambulances according to claim 5, characterized in that, The steps for global aggregation include: Batch retrieve all individual transit plans and generate a multi-objective planning matrix; The multi-objective planning matrix is ​​classified according to the primary selected hospital to generate a target hospital transfer matrix, and the resource lock status is pending confirmation. Once there are multiple [various types] in the matrix to be transferred For patients at the first level, perform resource adaptation verification on the target hospital, update the resource locking status to locked, and generate the actual target transfer set; During the transfer process, the status of locked resources is updated in real time, and the urgency of the target patient is calculated. Once the emergency level of the target patient is upgraded, the resource escalation process will be initiated immediately.

7. The time management optimization method for ambulances according to claim 6, characterized in that, The steps for resource compatibility verification include: Obtain each of the elements in the matrix to be transferred. The resource needs of patients at all levels are analyzed and grouped by resource type to generate a target resource set. Simultaneously, a set of available resources for the target hospital is generated. The target resource set is compared with the same type of resources in the set of available resources to determine whether the resources are suitable. If the number of available resources of all types is greater than the target number, then the resource is considered to be compatible and the resources are locked. Otherwise, it is determined that the resources do not match, the insufficient resources are filtered out, and it is determined whether there is a unique match; If it has a unique match, then for For patients in the first-level category, a rapid switchover process is triggered. If there is no unique match, integrate the corresponding insufficient resources. Patients at level 1 were ranked based on both urgency and estimated arrival time, with those at the top of the list. Level 1 patients have their resources locked, while other unlocked resources are... For patients at level 1, a tiered screening process will be implemented using alternative hospitals.

8. The time management optimization method for ambulances according to claim 7, characterized in that, The steps for calculating the expected deviation include: Construct a transport aggregation table, obtain the actual arrival time of the target patients, calculate the expected relative deviation, and for target patients who require conflict adjustment, calculate the adjusted relative deviation. A two-level classification threshold is set for the relative deviation of each emergency level, the relative deviation is classified, and the deviation level is marked. Obtain the deviation amount for each deviation level, calculate the distribution ratio of each deviation level under different emergency levels, filter the types of deviation concentration, and construct a deviation statistics table; A three-tiered cause classification system was established to match causes for severe deviation cases. For deviation clustering types, the fishbone diagram analysis method was used to locate the root cause. By integrating data with severe biases and bias set types, an optimized dataset is generated.

9. A time management display device for ambulances, used to implement the time management optimization method for ambulances as described in any one of claims 1-8, characterized in that, include: Emergency data collaboration terminal, computing center, and time display; The emergency data collaboration terminal is used to acquire the target patient's exclusive dataset and multi-dimensional dynamic data in real time. The transfer processing center is used to calculate the urgency of the target patient based on the judgment index, determine the urgency level of the target patient, calculate the estimated arrival time, perform global summary, generate a transfer matrix based on the target hospital, perform resource adaptation verification, and calculate the deviation value and analyze the cause after the transfer is completed. The time display is used to synchronously receive and display the estimated arrival time of the power transfer center.

10. The time management display device for ambulances according to claim 9, characterized in that: The computational power center includes a judgment module, a planning module, a verification and scheduling module, and a deviation analysis module; The judgment module is used to quantify judgment indicators, calculate the urgency of the target patient by combining weight allocation, classify the urgency level, and generate an emergency judgment report. The planning module is used to construct a planning dataset, perform hospital screening and route planning, calculate and verify the estimated arrival time, and generate individual transfer plans. The verification and scheduling module is used for global aggregation, constructing a transfer matrix based on the target hospital, and handling multiple... The system performs resource adaptation verification on the transfer matrix of patients at the primary level, generates the actual target transfer set, and monitors the transfer process in real time. The deviation analysis module is used to calculate and classify the relative deviation after the transfer is completed, and generate an optimized dataset.

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