Intelligent scheduling method and system for multi-mode process of emergency treatment

Abstract

The application provides an intelligent scheduling method and system for a multi-mode emergency treatment process, and relates to the technical field of medical resource scheduling. Firstly, the application collects real-time vital sign data and illness description data of emergency patients to form a multi-dimensional patient data set. Secondly, the application evaluates the emergency level and the required treatment mode type and obtains the medical resource state data of the emergency department. Thirdly, the application generates a multi-mode process scheduling instruction in combination with the emergency level, the required treatment mode type and the medical resource state data. Finally, the application executes the treatment process according to the multi-mode process scheduling instruction and continuously monitors the patient state and resource changes during the execution of the treatment process to update the multi-mode process scheduling instruction. The technical scheme provided by the application realizes standardized grading and dynamic scheduling planning by fusing multi-dimensional patient and resource data, and improves the response efficiency, resource utilization rate and process robustness of emergency treatment through closed-loop adaptive optimization.

Description

Technical Field

[0001] This application relates to the field of medical resource scheduling technology, and in particular to an intelligent scheduling method and system for multi-mode emergency treatment processes. Background Technology

[0002] With the continuous increase in the number of patients visiting emergency departments and the increasing complexity of diseases, there is an urgent need for efficient and accurate scheduling of treatment processes. This scenario requires the system to integrate multi-dimensional information such as patients' vital signs and disease descriptions in real time, quickly determine the degree of urgency and the required treatment mode, and coordinate dynamically changing medical resources such as doctors, equipment, and beds to generate the optimal treatment path and resource allocation plan in order to achieve intelligent and adaptive scheduling of multi-mode treatment processes.

[0003] Currently, the existing solution is an intelligent scheduling method based on an electronic triage system and a preset rule engine. This system automatically collects vital signs through integrated monitoring equipment and combines it with structured medical information entered at the triage station. It automatically classifies patients into priority levels and suggested treatment areas through a fixed rule base. At the same time, the system connects to the hospital information system (HIS) to obtain static or periodically updated resource data such as doctor schedules, equipment status, and bed occupancy. Based on a pre-set, relatively fixed resource matching logic, it assigns doctors and beds to patients and pushes a preset process guidance path.

[0004] However, in actual operation, the existing solution is insufficient in responding to dynamic changes in the patient's condition because its assessment of the patient's condition relies on relatively static rules and information at a single point in time; its resource scheduling logic is rigid and it is difficult to dynamically and collaboratively optimize and match according to the real-time changes in resource availability and the complex needs of multi-mode processes; its process path is preset and fixed, and it is impossible to replan and adjust in real time according to the evolution of the patient's condition and sudden resource situations during the treatment process. Summary of the Invention

[0005] This application provides an intelligent scheduling method and system for multi-mode emergency treatment processes to solve the problems of low scheduling efficiency, insufficient resource utilization, and untimely treatment response caused by the lag in patient status assessment, rigid resource matching, and lack of dynamic adjustment capability in the existing technology.

[0006] Firstly, this application provides an intelligent scheduling method for a multi-modal emergency care process, including: Collect real-time vital signs data and condition description data of emergency patients to form a multi-dimensional patient dataset; Based on the multi-dimensional patient dataset, the emergency level and required treatment mode type of emergency patients are assessed in real time, including resuscitation mode, surgical mode and observation mode. Acquire medical resource status data for the emergency department, including doctor availability, equipment availability, and treatment unit idle status. By combining the emergency level, the required treatment mode type, and the medical resource status data, a multi-mode process scheduling instruction is dynamically generated. The scheduling instruction is used to specify the treatment process path and resource allocation for emergency patients. According to the multi-mode process scheduling instructions, the treatment process is executed, and the status of emergency patients and changes in medical resources are continuously monitored during the execution of the treatment process in order to update the multi-mode process scheduling instructions.

[0007] Optionally, real-time vital signs data and condition description data of emergency patients are collected to form a multi-dimensional patient dataset, including: By connecting monitoring devices to emergency patients, the physiological signal streams of emergency patients are continuously acquired; The physiological signal stream is evaluated for signal quality, and segments that are distorted due to equipment interference and movement of emergency patients are identified and removed in order to obtain the target physiological signal segment. Based on the target physiological signal fragment, calculate the real-time vital signs data of the core physiological state of the emergency patient; At the same time, the text entered at the emergency triage desk is used to obtain the text describing the patient's condition based on the initial assessment by medical staff; Keyword extraction and standardized medical terminology conversion were performed on the described medical condition text to obtain medical condition description data; Real-time vital signs data and disease description data of the same emergency patient are correlated and combined to obtain a multi-dimensional patient dataset.

[0008] Optionally, based on the multi-dimensional patient dataset, the urgency level and required treatment mode type of emergency patients are assessed in real time. The treatment mode types include resuscitation mode, surgical mode, and observation mode, including: The real-time vital sign data is compared with multiple preset vital sign reference ranges to generate a vital sign deviation indication; From the disease description data, a first target keyword describing the severity of symptoms and a second target keyword describing the injured part of the body are identified to generate disease characteristic indicators; The vital sign deviation indicators are combined with the disease condition characteristics indicators to construct the state vector of the emergency patient; The state vector is input into a pre-established set of urgency determination rules for operation, so as to output the urgency level of the emergency patient. The set of urgency determination rules defines the correspondence between different combinations of state vectors and different urgency levels. The state vector and the information about the preliminary diagnosis and chief complaint in the disease description data are input into a pre-established set of pattern requirement analysis rules for operation, so as to output the type of treatment mode required for emergency patients. The set of pattern requirement analysis rules defines the correspondence between different clinical conditions and the required treatment mode types.

[0009] Optionally, medical resource status data of the emergency department can be obtained, including doctor availability, equipment availability, and treatment unit idle status, including: The system polls and queries the positioning terminals of medical staff deployed inside the emergency department. Based on the real-time location information returned by the positioning terminals and combined with the electronic schedule of the emergency department, it determines the current working status of each emergency doctor to obtain doctor on-duty status data containing doctor identifiers and corresponding statuses. Scan the network connection status and device self-test status code of critical treatment equipment inside the emergency department. When the network connection status is connected and the device self-test status code indicates that the device is functioning normally, the corresponding device is determined to be in an available state, so as to obtain device availability status data containing device identifier and corresponding status. Receive real-time data push from the emergency department bed management system. The real-time data push includes the current occupancy identifier and the estimated vacancy time of each treatment unit. Determine whether the treatment unit is currently idle based on the current occupancy identifier to obtain treatment unit idle status data containing the treatment unit identifier and the corresponding status. The doctor's on-site status data, the equipment's availability status data, and the treatment unit's idle status data are integrated according to timestamps to generate medical resource status data.

[0010] Optionally, by combining the urgency level, the required treatment mode type, and the medical resource status data, multi-mode process scheduling instructions are dynamically generated, including: Acquire multi-source information from emergency patients and fuse the information to form a multi-dimensional patient information set; Based on the multidimensional patient information set, the urgency level and treatment mode category of emergency patients are determined in real time. Real-time collection of availability status information for various medical resources within the emergency department; Based on the urgency level of treatment, the treatment mode category, and the availability status information, dynamic matching and path planning are performed to generate multi-mode process scheduling instructions.

[0011] Optionally, based on the treatment urgency level, the treatment mode category, and the availability status information, dynamic matching and path planning are performed to generate multi-mode process scheduling instructions, including: Based on the treatment mode category, query the predefined resource requirement template to determine the types of doctor professional skills, equipment types, and treatment unit functional requirements required to complete the treatment mode category, so as to obtain a list of resource requirements for emergency patients. Establish a dynamic ranking queue for emergency patients, and rank emergency patients from highest to lowest according to the urgency of their treatment. Based on the order results, for emergency patients in the dynamic sorting queue, resources are matched in the availability status information based on the corresponding resource demand list, and corresponding resource allocation records and resource waiting flags are generated according to the matching results. Integrate the resource allocation records and resource waiting markers corresponding to emergency patients to form a preliminary resource allocation plan; Based on the preliminary resource allocation plan, a specific movement path is planned for each emergency patient corresponding to a resource allocation record, from their current location to the allocated treatment unit. The resource allocation record is bound to the specific movement path to generate individual scheduling instructions; Integrate individual dispatch instructions corresponding to emergency patients to form multi-mode process dispatch instructions.

[0012] Optionally, according to the multi-mode process scheduling instructions, a treatment process is executed, and during the execution of the treatment process, the status of emergency patients and changes in medical resources are continuously monitored to update the multi-mode process scheduling instructions, including: The multi-mode process scheduling instructions are pushed to the medical and nursing workstations and mobile terminals in the emergency department to guide medical staff to perform corresponding treatment actions; During the rescue operation, real-time physiological data of emergency patients are periodically collected, and the real-time physiological data is compared with the real-time vital sign data on which the multi-mode process scheduling instructions are based to identify events of physiological state change in emergency patients. Simultaneously, it receives real-time resource status change events from the internal resource management system of the emergency department, including changes in doctor reception status, equipment usage status, and treatment unit occupancy status. Based on the physiological state change events, reassess and generate updated urgency levels and updated required treatment modal types for emergency patients; Based on the resource status change event, refresh and generate updated medical resource status data; Based on the updated urgency level, the updated required treatment mode type, and the updated medical resource status data, multi-mode process scheduling instructions are regenerated to obtain updated scheduling instructions. The updated scheduling instructions replace the original multi-mode process scheduling instructions and are then pushed to the medical workstations and mobile terminals in the emergency department to dynamically adjust the treatment process.

[0013] Secondly, this application provides an intelligent scheduling system for multi-modal emergency treatment processes, comprising: The data acquisition module is used to collect real-time vital signs data and condition description data of emergency patients to form a multi-dimensional patient dataset. The assessment module is used to assess the urgency level and required treatment mode type of emergency patients in real time based on the multi-dimensional patient dataset. The treatment mode type includes resuscitation mode, surgical mode and observation mode. The acquisition module is used to acquire medical resource status data of the emergency department, including doctor on-site status, equipment availability status, and treatment unit idle status. The generation module is used to dynamically generate multi-mode process scheduling instructions by combining the emergency level, the required treatment mode type and the medical resource status data. The scheduling instructions are used to specify the treatment process path and resource allocation for emergency patients. The update module is used to execute the treatment process according to the multi-mode process scheduling instructions, and continuously monitor the status of emergency patients and changes in medical resources during the execution of the treatment process in order to update the multi-mode process scheduling instructions.

[0014] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to realize an intelligent scheduling method for a multi-mode emergency treatment process as described in the first aspect above.

[0015] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an intelligent scheduling method for a multi-mode emergency treatment process as described in the first aspect.

[0016] This application achieves immediate and accurate assessment of the urgency and required treatment mode of emergency patients by real-time collection and fusion analysis of vital signs and condition description data. It also integrates the real-time status of resources such as doctors, equipment, and beds. Based on this, it collaboratively analyzes the dynamic needs of patients and the real-time availability of resources to generate appropriate treatment paths and resource allocation schemes. This solves the problems of unreasonable initial scheduling schemes, resource mismatch, and low process initiation efficiency caused by the lag in patient status assessment and rigid resource matching rules in the existing technology.

[0017] Furthermore, during the execution of the initial scheduling instructions, by continuously monitoring the evolution of the patient's physiological state and the real-time changes in medical resources, the scheduling instructions can be dynamically updated and replanned. This enables the entire scheduling process to adapt to fluctuations in the patient's condition and sudden resource situations, achieving online optimization and adjustment of the treatment process. This effectively overcomes the shortcomings of existing technologies, such as delayed scheduling schemes, insufficient resource utilization, and untimely treatment responses caused by fixed processes and a lack of feedback adjustment mechanisms.

[0018] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of an intelligent scheduling method for a multi-mode emergency treatment process provided in this application is shown; Figure 2 A schematic diagram of the structure of an intelligent scheduling system for a multi-mode emergency treatment process provided in this application is shown; Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 This application provides a flowchart of an intelligent scheduling method for a multi-mode emergency treatment process, such as... Figure 1 As shown, the method includes: Step 101: Collect real-time vital signs data and condition description data of emergency patients to form a multi-dimensional patient dataset.

[0025] Optionally, step 101 may specifically include: Step 1011: Continuously acquire the physiological signal stream of the emergency patient through a monitoring device connected to the patient.

[0026] Step 1012: Evaluate the signal quality of the physiological signal stream, identify and remove segments that are distorted due to equipment interference and movement of emergency patients, so as to obtain the target physiological signal segment.

[0027] Step 1013: Based on the target physiological signal fragment, calculate the real-time vital signs data of the core physiological state of the emergency patient.

[0028] Step 1014: Simultaneously, obtain the text describing the patient's condition based on the initial assessment by medical staff by using the text entered at the emergency triage desk.

[0029] Step 1015: Extract keywords and standardize medical terminology in the text describing the illness to obtain the illness description data.

[0030] Step 1016 involves associating and combining real-time vital signs data and disease description data for the same emergency patient to obtain a multi-dimensional patient dataset.

[0031] In this step, real-time vital signs data refers to the values ​​of key physiological indicators that are continuously measured from the patient's body and reflect their basic life activity status. It is used to quantitatively assess whether the patient's immediate physiological condition is stable. It is usually obtained by processing the raw physiological signals through medical monitoring equipment connected to the patient.

[0032] Disease description data refers to a standardized set of structured information about a patient's symptoms and preliminary diagnostic conclusions, used to qualitatively describe the patient's disease condition. It is obtained by extracting keywords and standardizing terminology from text information entered by medical staff.

[0033] A multidimensional patient dataset refers to a comprehensive data set formed by associating and integrating the real-time vital signs data and disease description data of the same patient according to the patient's identity. It is used to provide a complete information view including physiological indicators and clinical descriptions for subsequent analysis and decision-making.

[0034] Physiological signal streams refer to the continuous raw electrical signal waveforms that are continuously collected and output by monitoring equipment connected to emergency patients, reflecting one or more of the patient's physiological activities. These waveforms serve as the raw data basis for calculating various vital signs indicators.

[0035] The target physiological signal segment refers to the effective signal time period selected from the continuous physiological signal stream that meets the preset requirements, and is used to accurately calculate vital signs parameters.

[0036] The medical condition description text refers to the text information recorded in natural language by medical staff at the emergency triage station based on the patient's initial consultation and observation. This text is used to extract structured medical condition features and is obtained by medical staff through inputting the information into the triage system interface.

[0037] In this step, firstly, medical monitoring devices connected to the patient, such as electrocardiogram monitors and pulse oximeters, continuously acquire electrical signal waveforms representing physiological phenomena such as the patient's cardiac activity and pulse using their built-in sensors and analog-to-digital converters, thus obtaining a continuous physiological signal stream. Secondly, in order to obtain a reliable computational basis, these physiological signal streams need to be purified. A time-frequency analysis algorithm based on wavelet transform is applied to analyze the continuous signal waveforms. By calculating the energy distribution of the signal in different frequency sub-bands, segments with abnormal energy distribution and those that do not conform to the typical physiological signal spectrum characteristics are automatically identified. These segments are usually caused by power frequency interference from the equipment or the patient's body movement. These identified distorted segments are then removed, and segments whose signal characteristics conform to the preset physiological pattern are retained, thus obtaining the target physiological signal segments with qualified quality. Next, based on these high-quality target physiological signal segments, the preset feature point detection and parameter calculation algorithms are invoked. Specifically, for electrocardiogram signal segments, the Pan-Tompkins algorithm is used to detect R-wave peaks and calculate the intervals between adjacent R-waves. The instantaneous heart rate is then obtained by reciprocal calculation. For photoplethysmography (PPG) signal segments, the light intensity values ​​of the troughs and peaks are calculated using a peak-finding algorithm. Blood oxygen saturation is calculated based on the Lambert-Beer law. By performing this series of specific signal processing and calculations, a set of values ​​representing the patient's core physiological state is finally obtained, namely real-time vital sign data such as heart rate, blood pressure, and blood oxygen saturation. Meanwhile, in another parallel data stream, the hospital information system receives text records of patients' symptoms and initial impressions, which are entered by medical staff in natural language, submitted from the emergency triage terminal. To extract computer-processable key information from unstructured medical condition descriptions, a named entity recognition algorithm from natural language processing was employed. This algorithm, based on a pre-trained medical dictionary model, automatically scans the text, identifies and extracts clinically significant entity words describing symptom severity (e.g., severe pain, difficulty breathing) and body parts (e.g., chest, head). Subsequently, by querying a standardized medical terminology mapping table, the extracted words are automatically converted into unified medical terminology codes. For example, palpitations are mapped to the standard term palpitation T00278, thereby generating structured, standardized coded medical condition description data. Finally, based on the unified patient registration number obtained from the hospital information system, real-time vital sign data records belonging to the same patient and aligned in time are associated with structured disease description data records. These two types of information are then combined into a complete data record containing multidimensional fields through database write operations, thus forming the final multidimensional patient dataset for subsequent analysis.

[0038] For example, in the emergency department of People's Hospital in City A, when a patient with sudden chest pain is admitted, a monitoring device will be immediately attached. First, the device continuously generates raw electrical signal waveforms representing the patient's cardiac electrical activity and blood oxygen level, i.e., a physiological signal stream. Second, due to the patient's slight agitation caused by pain, some motion artifacts are mixed in with the physiological signal stream. Then, an automatic signal quality evaluation program is run to identify and filter out these interfering periods, retaining only the stable and clear signal segments as the target physiological signal segments. Based on these clean segments, the patient's current heart rate is accurately calculated to be 105 beats per minute. Blood oxygen saturation was 95%, generating real-time vital signs data. Simultaneously, text was entered into the computer: "Patient, male, 55 years old, chief complaint: sudden onset of oppressive pain in the anterior chest region, accompanied by profuse sweating." This description of the patient's condition was received by the system. The text was then analyzed, extracting keywords such as "sudden onset," "oppressive pain," and "anterior chest region." The "anterior chest region" was standardized to "retrosternal," generating structured description data of the patient's condition. Finally, the patient ID123's heart rate, blood oxygen data, and descriptive information such as retrosternal oppressive pain were linked and bound together to form a complete, multi-dimensional patient dataset containing numerical indicators and textual descriptions.

[0039] Step 102: Based on the multi-dimensional patient dataset, assess in real time the urgency level and required treatment mode type of emergency patients, including resuscitation mode, surgical mode and observation mode.

[0040] Optionally, step 102 may specifically include: Step 1021: Compare the real-time vital sign data with multiple preset vital sign reference ranges to generate a vital sign deviation indication.

[0041] Step 1022: Identify a first target keyword describing the severity of symptoms and a second target keyword describing the injured part of the body from the disease description data to generate a disease feature indication.

[0042] Step 1023: Combine the vital sign deviation indication with the disease condition characteristic indication to construct the state vector of the emergency patient.

[0043] Step 1024: Input the state vector into a pre-established set of emergency severity determination rules for operation, so as to output the emergency severity level of the emergency patient. The set of emergency severity determination rules defines the correspondence between different combinations of state vectors and different emergency severity levels.

[0044] Step 1025: Input the state vector and the content of preliminary diagnosis and chief complaint in the disease description data into a pre-established set of pattern demand analysis rules for operation, so as to output the treatment mode type required by the emergency patient. The set of pattern demand analysis rules defines the correspondence between different clinical conditions and the required treatment mode type.

[0045] In this step, the urgency level refers to a graded indicator used to quantify the severity of an emergency patient's condition and the order in which they need to be treated, and is used to determine the patient's priority in the scheduling queue.

[0046] Treatment mode type refers to the core treatment category that is divided according to the characteristics of the patient's condition and corresponds to different resource allocation and treatment paths. It is used to guide subsequent targeted resource matching and process planning. In this application, it specifically refers to rescue mode, surgical mode and observation mode, which are determined by analyzing the patient's clinical condition.

[0047] The emergency treatment mode is a type of treatment mode that refers to a category of treatment that requires immediate high-intensity life support and intervention. It is suitable for patients whose vital signs are extremely unstable or who are in immediate danger of death.

[0048] Surgical mode is a type of treatment mode that refers to the category of emergency treatment that requires surgical procedures. It is applicable to patients with clear surgical indications, such as acute trauma or acute abdomen.

[0049] The observation mode is a type of treatment mode that refers to a treatment category where the patient's condition is relatively stable and requires further examination and monitoring to clarify the diagnosis. It is suitable for patients who are not in immediate danger of death but need close observation.

[0050] The preset reference ranges for multiple vital signs refer to the upper and lower limits of the normal ranges for different vital sign parameters such as heart rate, blood pressure, and blood oxygen saturation, which are stored in the system in advance and are used as comparison standards to measure whether vital signs are abnormal. For example, the normal reference range for heart rate (adult) is preset to [60, 100] beats / minute, the normal reference range for systolic blood pressure is preset to [90, 140] mmHg, and the normal reference range for blood oxygen saturation is preset to not less than 95%.

[0051] The vital sign deviation indicator is a quantitative or qualitative marker that indicates the direction and degree of deviation of various real-time vital sign data from their corresponding preset normal reference range, and is used to reflect abnormalities in the patient's physiological state.

[0052] The primary target keywords refer to specific words or phrases identified from the disease description data that describe the severity of the patient's symptoms, such as severe, coma, and massive bleeding, which are extracted using natural language processing technology.

[0053] The second target keywords refer to specific words or phrases identified from the disease description data that describe the injured or diseased parts of the patient's body, such as head, chest, and abdomen, and are extracted using natural language processing technology.

[0054] Disease characteristic indicators refer to a set of identifying information, consisting of the first target keyword and the second target keyword, used to summarize the patient's main clinical symptoms and locations.

[0055] The state vector of an emergency patient refers to a structured combination of data that includes vital sign deviations representing physiological state and disease characteristics representing clinical symptoms, used to comprehensively characterize the patient's current overall condition.

[0056] The pre-established set of urgency determination rules refers to a collection of multiple judgment logics. Each logic defines a specific state vector combination pattern and a urgency level such as level one, level two, and level three. It is used to map the state vector to a specific urgency level. For example, IF state vector contains chest pain AND state vector contains ST segment elevation on ECG THEN urgency level = level one.

[0057] A pre-established set of pattern requirement analysis rules refers to a collection of multiple analysis logics. Each logic defines the correspondence between specific clinical condition characteristics and a required treatment mode type. It is used to infer the required treatment mode by combining the state vector and detailed condition description. For example: IF state vector contains right lower abdominal pain and muscle tension AND condition description contains positive McBurney's point tenderness THEN treatment mode type = surgical mode.

[0058] In this step, the threshold comparison algorithm is first called to process the real-time vital sign data in the multi-dimensional patient dataset. Each vital sign data, such as the heart rate value, is read in sequence and compared arithmetically with the upper and lower limits of the normal values ​​set for the vital sign data in multiple preset vital sign reference ranges. Based on the comparison results, the threshold comparison algorithm generates a qualitative deviation label for each vital sign data, such as heart rate higher than the normal range, blood pressure lower than the normal range, or blood oxygen saturation normal. The comparison results of all vital signs are summarized to form a set of structured vital sign deviation indicators. Secondly, keyword extraction technology from natural language processing is used to process the disease description data in the multi-dimensional patient dataset. This extraction technology is based on a predefined special dictionary containing words for symptom severity and body parts. It scans and matches patterns in the description data text, identifies and extracts all matching words describing symptom severity as the first target keywords, and identifies and extracts all matching words describing body parts as the second target keywords. Then, through data combination operations, the two types of extracted keywords are merged into a set to generate disease feature indicators. Next, data encapsulation and vector construction operations are performed. The generated vital sign deviation indicators and disease characteristic indicators are used as inputs, packaged and mapped according to a predefined structured data template, and the two types of indicators are filled into different dimensions of the vector, thereby constructing a unified state vector of emergency patients that can comprehensively represent the current pathophysiological state of the patient. Then, the inference engine based on production rules is launched, and a pre-established set of urgency determination rules is loaded. This set of rules consists of multiple logical rules in the form of drawing a conclusion if the conditions are met. The inference engine takes the constructed state vector as input facts and performs pattern matching with the condition part of each rule in the urgency determination rule set. When a rule is found whose conditions, such as the state vector containing low systolic blood pressure and impaired consciousness, are all satisfied by the current state vector, the inference engine triggers the urgency determination rule and outputs the urgency level defined in its conclusion part. Finally, the inference engine based on production rules is invoked again, but this time a pre-established set of pattern requirement analysis rules is loaded. The inference engine receives two inputs: a state vector and detailed text fragments of the patient's condition description data regarding the preliminary diagnosis and chief complaint. These two inputs are matched with rules in the pattern requirement analysis rule set. The conditions of these rules may comprehensively consider vector features and specific clinical expressions in the text. For example, when a rule is matched where the state vector indicates the presence of abdominal tenderness and muscle tension, and the patient's condition description text contains rebound tenderness, the inference engine triggers and outputs the treatment mode type corresponding to that pattern requirement analysis rule, such as surgical mode.

[0059] For example, following the specific implementation of the previous step, firstly, a multi-dimensional patient dataset for patient ID123 is obtained, which includes real-time vital sign data such as heart rate 105 beats / min and blood oxygen 95%, as well as condition description data such as retrosternal squeezing pain; secondly, the heart rate of 105 is compared with the preset normal range of heart rate 60-100 beats / min to generate a vital sign deviation indicator of high heart rate; then, blood oxygen 95% is compared with the normal range of blood oxygen saturation greater than or equal to 95% to generate a normal blood oxygen indicator; then, keywords are extracted from the condition description data, identifying squeezing pain as the first target keyword and retrosternal pain as the second target keyword, and combining them to form a condition feature indicator; subsequently, the vital sign deviation indicators such as high heart rate and normal blood oxygen are combined with the condition feature indicators such as squeezing pain and retrosternal pain to construct the patient's state vector; The state vector is then input into the urgency determination rule set, and one rule is triggered: if the state vector indicates the presence of chest pain symptoms accompanied by abnormal heart rate, the urgency level is level two; therefore, the system outputs that the patient's urgency level is level two; finally, the same state vector, along with a text fragment describing the condition, is input into the pattern requirement analysis rule set, and a rule is matched: if the symptoms are oppressive pain behind the sternum and the vital signs are abnormal, the resuscitation mode should be given priority and acute myocardial infarction should be excluded; therefore, the patient's required treatment mode is determined to be the resuscitation mode.

[0060] Step 103: Obtain medical resource status data for the emergency department, including doctor availability, equipment availability, and treatment unit idle status.

[0061] Optionally, step 103 may specifically include: Step 1031: Poll the positioning terminals of medical staff deployed inside the emergency department. Based on the real-time location information returned by the positioning terminals and the electronic schedule of the emergency department, determine the current working status of each emergency doctor to obtain doctor on-duty status data containing doctor identifiers and corresponding statuses.

[0062] Step 1032: Scan the network connection status and device self-test status code of critical treatment equipment inside the emergency department. When the network connection status is connected and the device self-test status code indicates that the device is functioning normally, the corresponding device is determined to be available, so as to obtain device availability status data containing device identifier and corresponding status.

[0063] Step 1033: Receive real-time data push from the emergency department bed management system. The real-time data push includes the current occupancy identifier and the estimated vacancy time of each treatment unit. Determine whether the treatment unit is currently idle based on the current occupancy identifier to obtain treatment unit idle status data containing the treatment unit identifier and the corresponding status.

[0064] Step 1034: Integrate the doctor's on-site status data, the equipment's available status data, and the treatment unit's idle status data according to timestamps to generate medical resource status data.

[0065] In this step, medical resource status data refers to a comprehensive dataset describing the current availability of core resources in the emergency department, used to provide real-time resource supply information for intelligent scheduling decisions.

[0066] The "on-duty status" refers to an indicator that describes whether an emergency room doctor is currently on duty and able to receive new patients, and is used to determine whether doctor resources are available.

[0067] Equipment availability status refers to an indicator that describes whether a critical medical device is currently in a state of normal operation, without faults, and not occupied, and is used to determine whether equipment resources can be called upon.

[0068] The idle status of a treatment unit refers to an indicator that describes whether a treatment unit, such as a resuscitation room bed or an observation bed, is currently not occupied by a patient and has been disinfected and cleaned. It is used to determine whether the bed resources are available to receive new patients.

[0069] Medical staff positioning terminals refer to electronic devices, such as name tags or smartphones, worn or carried by emergency department medical staff that can report their geographical location information in real time. They are used to track the real-time on-duty location of medical staff and are deployed through wireless indoor positioning technology.

[0070] Real-time location information refers to the coordinate data obtained from the positioning terminal of medical staff at a certain moment, which represents the coordinates of the medical staff in a specific area inside the emergency department, such as the triage desk or the resuscitation room, and is used to analyze their activity range.

[0071] Current work status refers to a description of a doctor's specific work situation at any given moment, such as being on duty and waiting, seeing patients, or having finished get off work. It is used to refine the doctor's availability and is obtained through logical analysis by combining their real-time location with the electronic scheduling plan.

[0072] Doctor identification refers to information used to uniquely distinguish different doctors in the system, such as employee number or name, and is used to accurately identify a specific doctor in the data. It is obtained from the hospital's human resources system.

[0073] Network connectivity status refers to whether the communication link between a medical device and the hospital's internal equipment management network is connected or disconnected. It is used to make a preliminary judgment on whether the device is online and is detected by network scanning technology.

[0074] Device self-test status codes refer to a set of internal diagnostic codes generated by medical devices after startup or periodic self-tests, representing whether each of their functional modules is normal. They are used to make in-depth judgments on whether the device's hardware and software functions are intact and can be obtained by querying the status interface provided by the device.

[0075] Device identification refers to information used to uniquely distinguish different medical devices in the system, such as device serial number or asset number, and is used to accurately identify a specific device in the data. It is obtained from the hospital asset management system.

[0076] Real-time data push refers to the latest messages about changes in bed occupancy proactively sent by the emergency department bed management system to the dispatch system, so that the dispatch system can promptly detect changes in bed resources.

[0077] The current occupancy indicator is a Boolean value indicating whether a treatment unit is currently occupied by a patient, such as occupied or free. It is used to directly determine the immediate availability of beds and is obtained from the database records of the bed management system.

[0078] Expected availability time refers to the predicted point in the future when a currently occupied treatment unit will become available. It is used for proactive resource allocation and is calculated and pushed by the bed management system based on the patient's expected discharge time.

[0079] Treatment unit identifiers are information used to uniquely distinguish different treatment units, such as beds, in the system. These identifiers, such as room numbers and bed numbers, are used to accurately identify specific treatment units in the data and are obtained from the bed management system.

[0080] In this step, polling technology is first used to periodically send requests to the positioning beacon network deployed inside the emergency department to obtain the latest real-time location information of all registered medical staff positioning terminals. Then, a rule matching engine is invoked to compare the real-time location of each doctor with the theoretical work area of ​​the doctor for the current time period in the electronic schedule of the emergency department synchronized from the hospital information system. Based on the comparison results, the rule matching engine determines the doctor's current work status, such as on duty or possibly absent from duty, and associates each doctor's doctor identifier with its determined status to form doctor on-duty status data. Secondly, network device discovery and status scanning technology is used to scan specific network segments in the emergency department, actively probing all online devices. For identified critical treatment equipment such as ventilators and defibrillators, their network connection status is obtained. For devices that are already connected, a query command is sent to them through the device communication protocol to obtain the device's internal self-test status code. Then, the self-test status code is interpreted. If the code indicates that all functional modules are normal, the device is determined to be in an available state. The device identifier of each device is associated with its availability determination result to form device availability status data. Simultaneously, through message listening and subscription technology, the system continuously monitors the real-time data push message stream from the emergency department bed management system. When a bed status is updated, the system receives the push message, which typically includes the treatment unit identifier, the current occupancy identifier, and the estimated availability time. The system directly reads the current occupancy identifier. If the identifier is "idle," the treatment unit is determined to be in an idle state. If it is "occupied," it is marked as "in use" based on the estimated availability time. This determination result is then associated with the treatment unit identifier to form treatment unit idle status data. Finally, a multi-source time series data integration operation is performed. It receives doctor on-site status data, equipment availability status data, and treatment unit idle status data generated by three parallel processes. Each data point is marked with a unified timestamp indicating that it was received. Then, according to a predefined data structure, the three types of data are merged into a unified data object or database table, thereby generating a complete and time-consistent medical resource status data, providing an instantaneous resource snapshot for global scheduling decisions.

[0081] For example, following the specific implementation of the previous step, after patient ID123 is determined to need to enter the resuscitation mode, the real-time acquisition of resource status is initiated simultaneously. Secondly, by polling the positioning beacon network within the department, the real-time location of doctor Zhang (employee number ZD001) is obtained as being in resuscitation zone 1. Combined with his electronic schedule, his current work status is determined to be on duty and ready to be deployed, generating a structured record. At the same time, the device network is actively scanned, and the RESP-01 ventilator is found to be online. By querying its self-test interface, a 0x00 normal status code is obtained, thus determining that the device is available and generating another status record. At this moment, a real-time push message is received from the bed management system, showing that the status of resuscitation bed R-Bed-02 has changed to idle, and a treatment unit status record is immediately generated accordingly. Finally, these three status records, which are generated almost simultaneously and have precise timestamps, are integrated to form a complete medical resource status data reflecting the real-time availability of doctors, equipment, and beds.

[0082] Step 104: Based on the emergency level, the required treatment mode type, and the medical resource status data, dynamically generate multi-mode process scheduling instructions. The scheduling instructions are used to specify the treatment process path and resource allocation for emergency patients.

[0083] Optionally, step 104 may specifically include: Step 1041: Obtain multi-source information of emergency patients and perform information fusion to form a multi-dimensional patient information set.

[0084] Step 1042: Based on the multidimensional patient information set, determine the urgency level and treatment mode category of emergency patients in real time.

[0085] Step 1043: Collect real-time availability information of various medical resources within the emergency department.

[0086] Step 1044: Based on the treatment urgency level, treatment mode category, and availability status information, perform dynamic matching and path planning to generate multi-mode process scheduling instructions.

[0087] Optionally, step 1044 may specifically include the following steps: Based on the treatment mode category, query a predefined resource requirement template to determine the types of doctor professional skills, equipment types, and treatment unit functional requirements required to complete the treatment mode category, thereby obtaining a resource requirement list for emergency patients; establish a dynamic sorting queue for emergency patients, and sort emergency patients from high to low according to the urgency level of their treatment; based on the order result, perform resource matching in the availability status information for emergency patients in the dynamic sorting queue based on the corresponding resource requirement list, and generate corresponding resource allocation records and resource waiting markers based on the matching results; integrate the resource allocation records and resource waiting markers corresponding to emergency patients to form a preliminary resource allocation scheme; based on the preliminary resource allocation scheme, plan a specific movement path from the current location to the assigned treatment unit for each emergency patient corresponding to a resource allocation record; bind the resource allocation record with the specific movement path to generate individual scheduling instructions; integrate the individual scheduling instructions corresponding to emergency patients to form a multi-mode process scheduling instruction.

[0088] In this step, the multi-mode process scheduling instruction refers to a comprehensive operational instruction that includes a specific action route and allocated medical resources for a particular emergency patient, used to guide medical staff to perform treatment according to the predetermined process.

[0089] The treatment process path refers to the specific spatial route and key node sequence planned for an emergency patient from their current location, such as the triage area, to the target treatment unit, such as a designated emergency room bed. It is used to guide the rapid transfer of patients and is calculated through a path planning algorithm.

[0090] Resource allocation refers to the decision-making process during scheduling, in which currently available and qualified specific doctors, equipment, and treatment units are assigned to a particular emergency patient. This process is determined through resource matching operations.

[0091] Availability status information refers to the synonymous expression of medical resource status data in the context of scheduling decisions, that is, the status information reflecting whether doctors, equipment, and treatment units are currently available for immediate use.

[0092] A predefined resource requirement template is a pre-set configuration file that describes the types of doctors, equipment, and functional requirements of treatment units typically required to complete each type of treatment mode, such as resuscitation mode. It is used to convert treatment modes into specific resource lists, which can be obtained by consulting the template. For example, doctors: ACLS qualification; equipment: electrocardiogram monitor, defibrillator, ventilator; beds: standard resuscitation bed.

[0093] Physician professional skill types refer to the professional qualifications that a doctor possesses that match a specific treatment model, such as cardiopulmonary resuscitation qualifications or trauma surgery qualifications, which are used to screen suitable doctors during the matching process.

[0094] Equipment type refers to the classification of medical equipment according to its function, such as ventilator, defibrillator, and monitor, to clarify the type of equipment required.

[0095] The functional requirements of a treatment unit refer to the functional conditions that a treatment unit needs to meet, such as emergency beds with central oxygen supply and isolation observation rooms, which are used to screen treatment sites that meet the requirements.

[0096] The resource requirements list is a list extracted from a predefined resource requirements template for a specific emergency patient, based on the type of treatment required. It specifies the exact types and requirements of the various resources needed and is used to guide subsequent precise matching.

[0097] A dynamic sorting queue is a virtual queue that sorts all emergency patients awaiting treatment in real time according to their urgency level. Patients with higher urgency levels are placed at the front of the queue. This queue is used to determine the priority of resource allocation and is established through a sorting algorithm.

[0098] A resource allocation record is a record that shows the result of successfully matching and locking specific doctors, equipment, treatment units, and other resources for a certain emergency patient. It is used to record the established allocation relationship and is generated through a successful resource matching operation.

[0099] A resource waiting flag is an identifier attached to an emergency patient's record, indicating that the patient cannot be allocated resources temporarily due to insufficient resources and must enter a waiting state. It is generated when a match fails.

[0100] A preliminary resource allocation plan refers to a global view of how resources are currently allocated at a certain decision point, formed by integrating all emergency patients' resource allocation records and resource waiting tags. It reflects the scheduling arrangements at this moment and is formed by integrating all records and tags.

[0101] The specific movement path refers to the detailed walking or transfer route planned for the patient on the internal map of the emergency department, from the starting point to the destination of the assigned treatment unit, including the passages and access control points along the way, which is calculated by the path planning algorithm.

[0102] Individual dispatch instructions refer to a complete instruction package generated for a single emergency patient, containing their own resource allocation record and specific movement path. It is the basic unit that constitutes global instructions and is generated by binding the two together.

[0103] In this step, the information fusion operation is first performed to aggregate and associate the urgency level, the required treatment mode type, and the core information in the multi-dimensional patient dataset, and package them into a multi-dimensional patient information set that represents the complete condition and needs of the patient. Here, the treatment urgency level and treatment mode category are synonymous expressions of the urgency level and the required treatment mode type, respectively. Secondly, through data query operations, based on the treatment mode category in the current multidimensional patient information set, the predefined resource requirement template is searched in the database, stored in the form of key-value pairs, and the value corresponding to the category is extracted, that is, a detailed description of the type of doctor's professional skills, equipment type and treatment unit functional requirements necessary to complete this type of treatment, thereby generating a resource requirement list exclusive to the patient. Next, the priority sorting algorithm is called to read the urgency level of all emergency patients waiting to be scheduled, quickly sort these patients in descending order, and establish a dynamic sorting queue in this order to ensure that the most critical patients are treated first. Then, the sequential iteration and resource matching process is initiated. Following the order of the dynamically sorted queue, each patient in the queue is processed sequentially. For the patient currently being processed, their resource requirement list is used as the filtering condition, and a multi-condition parallel search is performed in the availability status information. The search process is as follows: matching the professional skill type in the doctor information, matching the equipment type and its status as available in the equipment information, and matching the functional requirements and its status as idle in the treatment unit information. If a resource combination that meets all the conditions can be found, a resource locking operation is performed, the combination is marked as reserved and temporarily removed from the availability pool, and a resource allocation record is generated that records the correspondence between the patient and the allocated resources. If a resource combination that meets all the conditions cannot be found, no resources are allocated to the patient, and only a resource waiting mark is added to their record. This process is performed once for all patients in the queue. Then, a data aggregation operation is performed to collect the resource allocation records and resource waiting tags generated for all patients in this iteration, organize them according to patient identification, and form a global view reflecting the current resource allocation and waiting status, i.e., the preliminary resource allocation plan. Then, for each resource allocation record in the preliminary resource allocation plan, the path planning algorithm is activated. Starting from the patient's current known physical location and ending at the location of the assigned treatment unit, based on the pre-loaded digital floor plan of the emergency department, an optimal or suboptimal route that avoids fixed obstacles, has a shorter distance, or has higher passage efficiency is calculated, i.e., the specific movement path. Subsequently, the instruction encapsulation operation is performed, binding each resource allocation record, including the allocated doctor, equipment, and treatment unit information, with its corresponding specific movement path information, and packaging it into a complete, independent, and executable instruction package, namely an individual scheduling instruction. Finally, through the instruction set operation, all the individual scheduling instructions that need to be executed are gathered together to form a global, structured instruction list or instruction set, thereby generating the final multi-mode process scheduling instruction.

[0104] For example, following the specific implementation of the previous step, firstly, the assessment results and real-time resource status of patient ID123 (Level II Emergency, Resuscitation Mode) and patient ID124 (Level III Emergency, Observation Mode) are obtained; secondly, a predefined resource requirement template is queried according to the resuscitation mode to generate a resource requirement list for ID123: requiring an ACLS-qualified doctor, ventilator, monitor, and standard resuscitation bed; then, a dynamic sorting queue is established, placing ID123 before ID124 according to the urgency level; then, resource matching is performed in this order, successfully matching ID123 with Dr. Zhang who is on duty, the available ventilator RESP-01, and the vacant resuscitation bed R-Bed-02, generating a resource allocation record; subsequently, all records are integrated to form a preliminary resource allocation plan, planning a specific movement path for ID123 from the triage station to R-Bed-02, and binding the path with the resource allocation record to generate individual scheduling instructions; finally, all individual instructions for patients are integrated to form a complete dynamic multi-mode process scheduling instruction set.

[0105] Step 105: Execute the treatment process according to the multi-mode process scheduling instruction, and continuously monitor the status of emergency patients and changes in medical resources during the execution of the treatment process to update the multi-mode process scheduling instruction.

[0106] Optionally, step 105 may specifically include: Step 1051: Push the multi-mode process scheduling instructions to the medical and nursing workstations and mobile terminals in the emergency department to guide medical staff to perform corresponding treatment actions.

[0107] Step 1052: During the execution of the rescue operation, real-time physiological data of emergency patients are periodically collected, and the real-time physiological data is compared with the real-time vital sign data on which the multi-mode process scheduling instruction is based to identify physiological state change events of emergency patients.

[0108] Step 1053: Simultaneously receive real-time resource status change events from the internal resource management system of the emergency department. The resource status change events include changes in doctor admission status, equipment usage status, and treatment unit occupancy status.

[0109] Step 1054: Based on the physiological state change events, reassess and generate the updated emergency level and the updated required treatment mode type for emergency patients.

[0110] Step 1055: Based on the resource status change event, refresh and generate updated medical resource status data.

[0111] Step 1056: Based on the updated urgency level, the updated required treatment mode type, and the updated medical resource status data, regenerate the multi-mode process scheduling instructions to obtain the updated scheduling instructions.

[0112] Step 1057: Replace the original multi-mode process scheduling instruction with the updated scheduling instruction, and push it to the medical workstations and mobile terminals in the emergency department again to dynamically adjust the treatment process.

[0113] In this step, the emergency patient status refers to the changes in the patient's physiological indicators and clinical manifestations over time during the treatment process, which is used to determine whether the treatment strategy needs to be adjusted due to the evolution of the condition.

[0114] Changes in medical resources refer to the dynamic changes in the availability of core resources such as doctors, equipment, and treatment units within the emergency department over time. This information is used to determine whether the current scheduling plan needs to be adjusted due to resource changes, and is obtained by monitoring status change events in the resource management system.

[0115] Real-time physiological data refers to the latest physiological indicators continuously collected from monitoring devices connected to emergency patients during the treatment process, reflecting the patient's current immediate physiological condition.

[0116] Physiological status change event refers to an internal system alarm signal that indicates a clinically significant deterioration in one or more key physiological indicators of an emergency patient. It is used to trigger a reassessment of the patient's condition and is generated by comparing the currently collected real-time physiological data with historical baseline data using a threshold comparison.

[0117] Real-time resource status change events refer to messages proactively sent by various resource management systems within the emergency department, informing the system that the status of a specific resource has changed. These messages enable the scheduling system to perceive changes in resource availability in real time and are received through message subscription and listening technologies.

[0118] Doctor's status change is a type of real-time resource status change event, referring to a change in a doctor's status from standby to seeing patients or from seeing patients to being available for seeing patients.

[0119] Equipment usage status change is a type of real-time resource status change event, which refers to a change in the status of a device from idle and available to in use or from in use to fault, triggered by the equipment management system or usage records.

[0120] Changes in the occupancy status of a treatment unit are a type of real-time resource status change event, referring to changes in the status of a treatment unit from occupied to idle or vice versa, which are obtained through updates from the bed management system.

[0121] The updated scheduling instructions refer to the new version of the multi-mode process scheduling instructions generated by re-running the scheduling decision logic during the execution of the treatment process, based on newly monitored changes in patient status and resources. These instructions are used to replace or supplement the original instructions to guide subsequent actions.

[0122] In this step, the generated multi-mode process scheduling instructions are first distributed in real time in a structured message format to relevant fixed medical and nursing workstations in the emergency department, such as triage desks, nurse station computers, and mobile terminals carried by medical staff, such as tablets and dedicated PDAs. The application of these terminal devices parses and displays the details of the instructions, thereby guiding medical staff to begin to execute the treatment actions specified in the instructions, such as transferring patients to designated beds or preparing specific equipment. Secondly, while the rescue operation is underway, the latest real-time physiological data is periodically read from the monitoring device connected to the patient through a timed data collection service, such as every 30 seconds. The difference comparison and threshold judgment algorithm is called to compare the value of each newly collected real-time physiological data, such as the current heart rate, with the corresponding real-time vital sign data, such as the initial heart rate, stored in the history record when the original scheduling instruction was generated. The magnitude of the change is calculated, and when the magnitude of the change of any indicator exceeds the preset clinically significant change threshold for that indicator, it is determined that a physiological state change event has occurred, and an internal alarm containing the event type and details is generated. At the same time, through the event listening and message subscription mechanism, the system continuously listens to the message channels from various resource management systems within the hospital. When the doctor workstation system reports that a doctor has started or ended a consultation, the equipment management system reports that a piece of equipment has been activated or has malfunctioned, or the bed management system reports that a bed has been occupied or vacated, the system will proactively send out real-time resource status change event messages. Then, when at least one physiological state change event is detected or at least one real-time resource status change event is received, a complete scheduling instruction update judgment process is triggered. In the first branch, for physiological state change events, the latest information of the patient is re-collected, and the urgency and treatment mode assessment logic is re-invoked. Specifically, the patient's current information, including the latest real-time physiological data, is used as input to re-execute all the assessment operations defined above, thereby reassessing and generating the updated urgency level and the updated required treatment mode type for emergency patients. In the second branch, for real-time resource status change events, the resource status data refresh procedure is triggered. Based on the received event content, the status field of the affected resource in its internally maintained medical resource status data copy is updated in real time. For example, when an event is received that Doctor A has started seeing patients, its status is updated to busy; when an event is received that Bed B has become available, its status is updated to idle. The updated data copy constitutes the updated medical resource status data. Then, the outputs of these two branches are integrated, and the updated urgency level, the updated required treatment mode type, and the updated medical resource status data are used as a new set of inputs. The operation of generating all the dynamic multi-mode process scheduling instructions defined in the previous step is re-executed. This process will re-match resources and plan paths based on the latest patient needs and resource supply, thereby generating a new set of updated scheduling instructions that are more adapted to the current situation. Finally, the instruction replacement and re-push operation is performed. The newly generated updated scheduling instruction replaces the original multi-mode process scheduling instruction that is currently being executed, either wholly or partially. Then, the updated scheduling instruction is immediately sent to the relevant medical workstations and mobile terminals through message push technology, so that all medical staff can be informed of the plan change in a timely manner and adjust their treatment actions according to the new instructions, thereby completing a dynamic adjustment of the treatment process.

[0123] For example, following the specific implementation of the previous step, patient ID123, accompanied by doctor ZD001, is heading to the R-Bed-02 resuscitation bed according to the dispatch instructions. Suddenly, the monitoring equipment shows that the heart rate drops sharply to 45 beats / min. Through periodic collection and comparison, the system identifies a physiological state change event of severe bradycardia. Almost simultaneously, a message is received that the ventilator RESP-01 originally assigned to ID123 has suddenly malfunctioned, triggering a change in equipment usage status event. The update process is then immediately initiated. On the one hand, based on the latest data such as the patient's suddenly dropped heart rate, it is necessary to reassess and upgrade the emergency level from level two to level one, while the treatment mode remains resuscitation mode. On the other hand, the resource data is refreshed, and RESP-01 is marked as faulty. Then, with Level 1 Emergency, Rescue Mode, and the latest resource data including the faulty RESP-01 as input, a new scheduling decision is made. The new decision may match another available ventilator, RESP-02, for patient ID123 and plan a path to the closer resuscitation bed, R-Bed-01, generating an updated scheduling instruction. Finally, the new instruction overwrites the original instruction and is immediately pushed out, and the doctor's mobile terminal, ZD001, will immediately receive the update: the target has changed to R-Bed-01, and the ventilator RESP-02 has been used, thus quickly adjusting the treatment action.

[0124] Figure 2 This application provides a schematic diagram of the structure of an intelligent scheduling system for multi-mode emergency treatment processes, as shown in the diagram. Figure 2 As shown, the system includes: The acquisition module 21 is used to collect real-time vital signs data and condition description data of emergency patients to form a multi-dimensional patient dataset; The assessment module 22 is used to assess the urgency level and required treatment mode type of emergency patients in real time based on the multi-dimensional patient dataset. The treatment mode type includes resuscitation mode, surgical mode and observation mode. The acquisition module 23 is used to acquire medical resource status data of the emergency department, including doctor on-site status, equipment availability status, and treatment unit idle status. The generation module 24 is used to dynamically generate multi-mode process scheduling instructions by combining the emergency level, the required treatment mode type and the medical resource status data. The scheduling instructions are used to specify the treatment process path and resource allocation for emergency patients. The update module 25 is used to execute the treatment process according to the multi-mode process scheduling instructions, and continuously monitor the status of emergency patients and changes in medical resources during the execution of the treatment process in order to update the multi-mode process scheduling instructions.

[0125] Figure 2 The aforementioned intelligent scheduling system for multi-mode emergency treatment processes can execute... Figure 1 The implementation principle and technical effects of the intelligent scheduling method for a multi-mode emergency treatment process described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit performs operations in the intelligent scheduling system for a multi-mode emergency treatment process described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0126] In one possible design, Figure 2 The intelligent scheduling system for a multi-mode emergency treatment process shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0127] The processing component 32 is used for the above Figure 1 The embodiment describes an intelligent scheduling method for a multi-mode emergency treatment process.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An intelligent scheduling method for multi-mode emergency treatment processes, characterized in that, include: Collect real-time vital signs data and condition description data of emergency patients to form a multi-dimensional patient dataset; Based on the multi-dimensional patient dataset, the emergency level and required treatment mode type of emergency patients are assessed in real time, including resuscitation mode, surgical mode and observation mode. Acquire medical resource status data for the emergency department, including doctor availability, equipment availability, and treatment unit idle status. By combining the emergency level, the required treatment mode type, and the medical resource status data, a multi-mode process scheduling instruction is dynamically generated. The scheduling instruction is used to specify the treatment process path and resource allocation for emergency patients. According to the multi-mode process scheduling instructions, the treatment process is executed, and the status of emergency patients and changes in medical resources are continuously monitored during the execution of the treatment process in order to update the multi-mode process scheduling instructions.

2. The method according to claim 1, characterized in that, Real-time vital signs and condition descriptions of emergency patients are collected to form a multi-dimensional patient dataset, including: By connecting monitoring devices to emergency patients, the physiological signal streams of emergency patients are continuously acquired; The physiological signal stream is evaluated for signal quality, and segments that are distorted due to equipment interference and movement of emergency patients are identified and removed in order to obtain the target physiological signal segment. Based on the target physiological signal fragment, calculate the real-time vital signs data of the core physiological state of the emergency patient; At the same time, the text entered at the emergency triage desk is used to obtain the text describing the patient's condition based on the initial assessment by medical staff; Keyword extraction and standardized medical terminology conversion were performed on the described medical condition text to obtain medical condition description data; Real-time vital signs data and disease description data of the same emergency patient are correlated and combined to obtain a multi-dimensional patient dataset.

3. The method according to claim 1, characterized in that, Based on the aforementioned multi-dimensional patient dataset, the urgency level and required treatment mode type of emergency patients are assessed in real time. The treatment mode types include resuscitation mode, surgical mode, and observation mode, including: The real-time vital sign data is compared with multiple preset vital sign reference ranges to generate a vital sign deviation indication; From the disease description data, a first target keyword describing the severity of symptoms and a second target keyword describing the injured part of the body are identified to generate disease characteristic indicators; The vital sign deviation indicators are combined with the disease condition characteristics indicators to construct the state vector of the emergency patient; The state vector is input into a pre-established set of urgency determination rules for operation, so as to output the urgency level of the emergency patient. The set of urgency determination rules defines the correspondence between different combinations of state vectors and different urgency levels. The state vector and the information about the preliminary diagnosis and chief complaint in the disease description data are input into a pre-established set of pattern requirement analysis rules for operation, so as to output the type of treatment mode required for emergency patients. The set of pattern requirement analysis rules defines the correspondence between different clinical conditions and the required treatment mode types.

4. The method according to claim 1, characterized in that, Acquire medical resource status data for the emergency department, including doctor availability, equipment availability, and treatment unit idle status, including: The system polls and queries the positioning terminals of medical staff deployed inside the emergency department. Based on the real-time location information returned by the positioning terminals and combined with the electronic schedule of the emergency department, it determines the current working status of each emergency doctor to obtain doctor on-duty status data containing doctor identifiers and corresponding statuses. Scan the network connection status and device self-test status code of critical treatment equipment inside the emergency department. When the network connection status is connected and the device self-test status code indicates that the device is functioning normally, the corresponding device is determined to be in an available state, so as to obtain device availability status data containing device identifier and corresponding status. Receive real-time data push from the emergency department bed management system. The real-time data push includes the current occupancy identifier and the estimated vacancy time of each treatment unit. Determine whether the treatment unit is currently idle based on the current occupancy identifier to obtain treatment unit idle status data containing the treatment unit identifier and the corresponding status. The doctor's on-site status data, the equipment's availability status data, and the treatment unit's idle status data are integrated according to timestamps to generate medical resource status data.

5. The method according to claim 1, characterized in that, Based on the aforementioned urgency level, required treatment mode type, and medical resource status data, multi-mode process scheduling instructions are dynamically generated, including: Acquire multi-source information from emergency patients and fuse the information to form a multi-dimensional patient information set; Based on the multidimensional patient information set, the urgency level and treatment mode category of emergency patients are determined in real time. Real-time collection of availability status information for various medical resources within the emergency department; Based on the urgency level of treatment, the treatment mode category, and the availability status information, dynamic matching and path planning are performed to generate multi-mode process scheduling instructions.

6. The method according to claim 5, characterized in that, Based on the urgency level of treatment, the treatment mode category, and the availability status information, dynamic matching and path planning are performed to generate multi-mode process scheduling instructions, including: Based on the treatment mode category, query the predefined resource requirement template to determine the types of doctor professional skills, equipment types, and treatment unit functional requirements required to complete the treatment mode category, so as to obtain a list of resource requirements for emergency patients. Establish a dynamic ranking queue for emergency patients, and rank emergency patients from highest to lowest according to the urgency of their treatment. Based on the order results, for emergency patients in the dynamic sorting queue, resources are matched in the availability status information based on the corresponding resource demand list, and corresponding resource allocation records and resource waiting flags are generated according to the matching results. Integrate the resource allocation records and resource waiting markers corresponding to emergency patients to form a preliminary resource allocation plan; Based on the preliminary resource allocation plan, a specific movement path is planned for each emergency patient corresponding to a resource allocation record, from their current location to the allocated treatment unit. The resource allocation record is bound to the specific movement path to generate individual scheduling instructions; Integrate individual dispatch instructions corresponding to emergency patients to form multi-mode process dispatch instructions.

7. The method according to claim 1, characterized in that, According to the multi-mode process scheduling instructions, the treatment process is executed, and during the execution of the treatment process, the status of emergency patients and changes in medical resources are continuously monitored to update the multi-mode process scheduling instructions, including: The multi-mode process scheduling instructions are pushed to the medical and nursing workstations and mobile terminals in the emergency department to guide medical staff to perform corresponding treatment actions; During the rescue operation, real-time physiological data of emergency patients are periodically collected, and the real-time physiological data is compared with the real-time vital sign data on which the multi-mode process scheduling instructions are based to identify events of physiological state change in emergency patients. Simultaneously, it receives real-time resource status change events from the internal resource management system of the emergency department, including changes in doctor reception status, equipment usage status, and treatment unit occupancy status. Based on the physiological state change events, reassess and generate updated urgency levels and updated required treatment modal types for emergency patients; Based on the resource status change event, refresh and generate updated medical resource status data; Based on the updated urgency level, the updated required treatment mode type, and the updated medical resource status data, multi-mode process scheduling instructions are regenerated to obtain updated scheduling instructions. The updated scheduling instructions replace the original multi-mode process scheduling instructions and are then pushed to the medical workstations and mobile terminals in the emergency department to dynamically adjust the treatment process.

8. An intelligent dispatching system for multi-mode emergency treatment processes, characterized in that, include: The data acquisition module is used to collect real-time vital signs data and condition description data of emergency patients to form a multi-dimensional patient dataset. The assessment module is used to assess the urgency level and required treatment mode type of emergency patients in real time based on the multi-dimensional patient dataset. The treatment mode type includes resuscitation mode, surgical mode and observation mode. The acquisition module is used to acquire medical resource status data of the emergency department, including doctor on-site status, equipment availability status, and treatment unit idle status. The generation module is used to dynamically generate multi-mode process scheduling instructions by combining the emergency level, the required treatment mode type and the medical resource status data. The scheduling instructions are used to specify the treatment process path and resource allocation for emergency patients. The update module is used to execute the treatment process according to the multi-mode process scheduling instructions, and continuously monitor the status of emergency patients and changes in medical resources during the execution of the treatment process in order to update the multi-mode process scheduling instructions.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement an intelligent scheduling method for a multi-mode emergency treatment process as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements an intelligent scheduling method for a multi-mode emergency treatment process as described in any one of claims 1 to 7.

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