Medical health intelligent rescue management system

Abstract

The invention relates to an intelligent medical health rescue management system. The system comprises a first acquisition mechanism, a second acquisition mechanism, an object analysis device, a directional identification device and a distribution execution device. The medical health intelligent rescue management system is reliable in logic, stable in operation and capable of guaranteeing full utilization of emergency medical equipment with a limited number.

Description

Technical Field

[0001] The present invention relates to the field of smart medical care, and in particular to a medical health intelligent rescue management system. Background Art

[0002] In the design of smart medical care, the core work of the doctor's workstation is to collect, store, transmit, process and use the patient's health status and medical information. The doctor's workstation includes the work platform for the entire medical process, including outpatient and inpatient treatment, examination, diagnosis, treatment, prescription and medical advice, medical record, consultation, transfer, surgery, discharge, medical record generation, etc. The level of medical services can be improved by improving the application of technologies such as remote image transmission and large-scale data computing and processing in the construction process of digital hospitals. For example: remote visitation avoids direct contact between visitors and patients, prevents the spread of diseases, and shortens the recovery process; remote consultation supports the sharing of superior medical resources and cross-regional optimization configuration; automatic alarm monitors the patient's vital signs data and reduces the cost of critical care.

[0003] CN118484758A discloses an indoor anomaly detection emergency processing system based on artificial intelligence, including: an image generation and storage module, a monitoring video acquisition module, an anomaly detection processing module, an air detection processing module, a comprehensive information processing module, a user communication module, a medical rescue module and a fire emergency module. The technology also discloses an indoor anomaly detection emergency processing method based on artificial intelligence. Based on the above-mentioned indoor anomaly detection emergency processing system based on artificial intelligence, the spatiotemporal features of the acquired monitoring video frames are extracted by using the optimized spatiotemporal attention network model, and the anomaly and risk assessment index are obtained by combining the attention weight and dynamic weight allocation, and then the comprehensive score is obtained to formulate an emergency processing strategy. It can improve the accuracy of anomaly detection, enhance the accuracy of environmental detection, and formulate an emergency processing strategy based on the comprehensive score, thereby improving the efficiency of emergency processing.

[0004] CN118285767A discloses a patient vital signs monitoring system for emergency treatment. The vital signs monitoring module is directly mounted on the transport equipment. There is no need to use the relevant equipment separately in the prior art. The patient's heart rate, blood pressure and other related data can be directly monitored, which is convenient for medical staff on the ambulance to prepare rescue equipment and drugs in advance according to the vital signs data, greatly reducing the time for patients to receive medical treatment during emergency treatment, and actively striving for rescue opportunities. In addition, during the process of transferring patients to the hospital, the main control center can synchronously obtain the patient's relevant life data, and then sort the critical conditions of different patients, and dispatch medical resources in time according to the critical conditions, so that critically ill patients are not likely to accidentally miss rescue time and opportunities, and greatly improve the protection of the life safety of emergency patients.

[0005] CN118136224A discloses a pre-hospital emergency telemedicine rescue system for wearable devices, which relates to the field of emergency rescue technology. The pre-hospital emergency telemedicine rescue system for wearable devices includes a 5G module, which includes a server module, a management module, a data synchronization module, a rescue measure module, an emergency platform module and a user patient module. The 5G module is connected to the server module, and the server module is connected to the management module. Through smart wearable devices and AI intelligent technology, the emergency rescue channels of the smart wearable device SOS one-key call for help, video call for help and smart audio AI call for help are used. The emergency platform performs rescue measures on the patient, and quickly judges the patient's injury and condition, prioritizes emergency treatment, and pushes the on-site positioning information and basic patient information to the hospital emergency command and dispatch platform, thereby solving the problem of low work efficiency of medical staff, delays or even missing the golden treatment time for patients. Summary of the invention

[0006] In order to solve the technical problems in related fields, the present invention provides a medical health intelligent rescue management system, which adopts an artificial intelligence model to intelligently predict the number of passengers carried by the target subway line in the current time interval, and determines the number of emergency medical equipment required by the target subway line in the current time interval based on the intelligently predicted number of passengers carried by the target subway line in the current time interval. The determined number of emergency medical equipment required by the target subway line in the current time interval is positively correlated with the number of passengers carried by the target subway line in the current time interval, and before the current time interval arrives, the determined number of emergency medical equipment required by the target subway line in the current time interval is allocated to the target subway line, thereby ensuring that the full utilization of a relatively limited number of emergency medical equipment is met as much as possible. Different medical rescue needs of each subway line are met as much as possible.

[0007] According to the present invention, a medical health intelligent rescue management system is provided, the system comprising:

[0008] The first collection mechanism is used to obtain the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line, and output the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line as multiple configuration information of the target subway line;

[0009] The second collection mechanism is used to obtain the pieces of transportation data corresponding to each time interval before the current time interval of the target subway line, and the single piece of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval;

[0010] An object parsing device, used for parsing a deep convolutional network model, in which the deep convolutional network is learned multiple times to obtain a deep convolutional network after completing the multiple learnings and serving as the deep convolutional network model;

[0011] a directional identification device, connected to the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, for inputting multiple configuration information of the target subway line, the interval time length of each time interval and each portion of the carrying data corresponding to each time interval before the current time interval of the target subway line in parallel into the deep convolutional network model, and executing the deep convolutional network model to obtain the number of passengers carried by the target subway line in the current time interval output by the deep convolutional network model;

[0012] an allocation execution device connected to the directional identification device, and used to determine the number of emergency medical equipment required by the target subway line in the current time interval based on the number of passengers carried by the target subway line in the current time interval, wherein the determined number of emergency medical equipment required by the target subway line in the current time interval is positively correlated with the number of passengers carried by the target subway line in the current time interval;

[0013] Wherein, determining the number of emergency medical equipment required by the target subway line in the current time interval based on the number of passengers carried by the target subway line in the current time interval, and the number of emergency medical equipment required by the target subway line in the current time interval being positively associated with the number of passengers carried by the target subway line in the current time interval includes: before the current time interval arrives, executing the allocation of the number of emergency medical equipment required by the target subway line in the current time interval to the target subway line;

[0014] Among them, each set of transportation data corresponding to each time interval before the current time interval of the target subway line is obtained, and the single set of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval, including: each time interval before the current time interval and the current time interval form a complete time segment on the time axis and the interval time length of each time interval is equal.

[0015] It can be seen that the present invention mainly has the following significant technical effects:

[0016] First: the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line are obtained, and the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line are used as multiple configuration information of the target subway line, and each piece of transportation data corresponding to each time interval before the current time interval of the target subway line is obtained. The single piece of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval, thereby providing comprehensive and sufficient basic information for the subsequent intelligent prediction of the number of passengers carried by the target subway line in the current time interval;

[0017] Secondly: an object parsing device is introduced to parse a deep convolutional network model, in which the deep convolutional network is learned multiple times to obtain a deep convolutional network after multiple learning and used as the deep convolutional network model. The number of times the deep convolutional network is learned is proportional to the total length of the target subway line, so that deep convolutional network models with different structures are designed for different target subway lines;

[0018] Finally: the number of emergency medical equipment required by the target subway line in the current time interval is determined based on the intelligent prediction of the number of passengers carried by the target subway line in the current time interval. The determined number of emergency medical equipment required by the target subway line in the current time interval is positively correlated with the number of passengers carried by the target subway line in the current time interval, and before the current time interval arrives, the determined number of emergency medical equipment required by the target subway line in the current time interval is allocated to the target subway line, thereby ensuring full utilization of a relatively limited number of emergency medical equipment while meeting the different medical rescue needs of each subway line as much as possible.

[0019] The medical health intelligent rescue management system of the present invention has reliable logic and stable operation. Since the number of passengers carried by the target subway line in the current time interval can be intelligently predicted by the artificial intelligence model, the number of emergency medical equipment required by the target subway line in the current time interval is determined based on the intelligently predicted number of passengers carried by the target subway line in the current time interval, thereby ensuring full utilization of a relatively limited number of emergency medical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0021] Figure 1 Schematic diagram of the internal structure of a medical health intelligent rescue management system according to the first embodiment of the present invention.

[0022] Figure 2 Schematic diagram of the internal structure of a medical health intelligent rescue management system according to the second embodiment of the present invention.

[0023] Figure 3 Schematic diagram of the internal structure of a medical health intelligent rescue management system according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0024] At present, for crowd gathering areas in specific occasions, such as the interior of a target subway line, how to dynamically allocate limited medical resources, such as emergency medical equipment, according to the number of passengers carried by the target subway line in the future time interval is one of the technical problems that smart medical care needs to solve. At present, the fixed allocation form of a fixed number of emergency medical equipment in all time intervals is still used, which is easy to cause insufficient or excessive emergency medical equipment resources.

[0025] The embodiments of the medical health intelligent rescue management system of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1 This is a schematic diagram of the internal structure of a medical health intelligent rescue management system according to the first embodiment of the present invention, wherein the system comprises:

[0027] The first collection mechanism is used to obtain the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line, and output the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line as multiple configuration information of the target subway line;

[0028] For example, the first collection mechanism is used to obtain the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line, and output the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line as multiple configuration information of the target subway line. The first collection mechanism includes multiple collection execution units, which are used to respectively collect the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line;

[0029] The second collection mechanism is used to obtain the pieces of transportation data corresponding to each time interval before the current time interval of the target subway line, and the single piece of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval;

[0030] An object parsing device, used for parsing a deep convolutional network model, in which the deep convolutional network is learned multiple times to obtain a deep convolutional network after completing the multiple learnings and serving as the deep convolutional network model;

[0031] a directional identification device, connected to the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, for inputting multiple configuration information of the target subway line, the interval time length of each time interval and each portion of the carrying data corresponding to each time interval before the current time interval of the target subway line in parallel into the deep convolutional network model, and executing the deep convolutional network model to obtain the number of passengers carried by the target subway line in the current time interval output by the deep convolutional network model;

[0032] an allocation execution device connected to the directional identification device, and used to determine the number of emergency medical equipment required by the target subway line in the current time interval based on the number of passengers carried by the target subway line in the current time interval, wherein the determined number of emergency medical equipment required by the target subway line in the current time interval is positively correlated with the number of passengers carried by the target subway line in the current time interval;

[0033] Wherein, determining the number of emergency medical equipment required by the target subway line in the current time interval based on the number of passengers carried by the target subway line in the current time interval, and the number of emergency medical equipment required by the target subway line in the current time interval being positively associated with the number of passengers carried by the target subway line in the current time interval includes: before the current time interval arrives, executing the allocation of the number of emergency medical equipment required by the target subway line in the current time interval to the target subway line;

[0034] The process of obtaining the respective sets of transportation data corresponding to the respective time intervals before the current time interval of the target subway line, wherein the single set of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval, includes: the respective time intervals before the current time interval and the current time interval form a complete time segment on the time axis, and the interval time length of each time interval is equal;

[0035] The object parsing device is used to parse a deep convolutional network model, in which the deep convolutional network is learned multiple times to obtain a deep convolutional network after multiple learning and as the deep convolutional network model, including: the number of times the deep convolutional network is learned is proportional to the total length of the target subway line;

[0036] And wherein, each time interval before the current time interval and the current time interval form a complete time segment on the time axis and the interval time length of each time interval is equal includes: the interval time length of each time interval is greater than 1 day.

[0037] Figure 2 Schematic diagram of the internal structure of a medical health intelligent rescue management system according to the second embodiment of the present invention.

[0038] Compared to Figure 1 According to the second embodiment of the present invention, the medical health intelligent rescue management system may further include:

[0039] An information capture component is connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, respectively, and is used to measure the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively;

[0040] The information capture component is connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively, and is used to measure the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively. The information capture component includes a plurality of length measurement units, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively, so as to complete the measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively;

[0041] Wherein, the information capture component includes a plurality of length measurement units, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively, so as to complete the respective measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively, including: the plurality of length measurement units are a plurality of length sensors, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively, so as to complete the respective measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device respectively;

[0042] Wherein, the plurality of length measurement units are a plurality of length sensors, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, so as to complete the respective measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, including: the plurality of length sensors have the same structure;

[0043] Among them, the multiple length measurement units are multiple length sensors, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, so as to complete the measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, and also include: the multiple length sensors have the same length measurement upper limit value and length measurement lower limit value.

[0044] Figure 3 Schematic diagram of the internal structure of a medical health intelligent rescue management system according to the third embodiment of the present invention.

[0045] Compared to Figure 1 According to the third embodiment of the present invention, the medical health intelligent rescue management system may also include:

[0046] A data display component is connected to the plurality of length measurement units of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, and is used to synchronously display the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively;

[0047] Wherein, the data display component is respectively connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the plurality of length measurement units of the object analysis device, and is used to synchronously display the respective real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, and includes: the data display component is a touch display screen;

[0048] And wherein, the data display component is respectively connected to the multiple length measurement units of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, and is used to synchronously display the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, including: the data display component is a non-touch display screen or an LCD display array.

[0049] In addition, in the medical health intelligent rescue management system, multiple configuration information of the target subway line, the interval time length of each time interval, and each piece of transport data corresponding to each time interval before the current time interval of the target subway line are input into the deep convolutional network model in parallel, and the deep convolutional network model is executed to obtain the number of passengers carried by the target subway line in the current time interval output by the deep convolutional network model, including: performing hexadecimal data conversion processing on multiple configuration information of the target subway line, the interval time length of each time interval, and each piece of transport data corresponding to each time interval before the current time interval of the target subway line, and then synchronously inputting them into the deep convolutional network model;

[0050] And wherein, multiple configuration information of the target subway line, the interval time length of each time interval, and the respective transport data corresponding to each time interval before the current time interval of the target subway line are input into the deep convolutional network model in parallel, and the deep convolutional network model is executed to obtain the number of passengers carried by the target subway line in the current time interval output by the deep convolutional network model, which also includes: the number of passengers carried by the target subway line in the current time interval output by the deep convolutional network model is a hexadecimal numerical representation.

[0051] Those skilled in the art should understand that various improvements can be made to the device disclosed in the above invention without departing from the content of the invention. Therefore, the protection scope of the present invention should be determined by the content of the attached claims.

Claims

1. A medical health intelligent rescue management system, characterized in that: The system comprises: The first collection mechanism is used to obtain the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line, and output the total number of residents in the city where the target subway line is located, the total length of the target subway line, the number of stops of the target subway line, and the number of carriages of the target subway line as multiple configuration information of the target subway line; The second collection mechanism is used to obtain the pieces of transportation data corresponding to each time interval before the current time interval of the target subway line, and the single piece of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval; An object parsing device, used for parsing a deep convolutional network model, in which the deep convolutional network is learned multiple times to obtain a deep convolutional network after completing the multiple learnings and serving as the deep convolutional network model; a directional identification device, connected to the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, for inputting multiple configuration information of the target subway line, the interval time length of each time interval and each portion of the carrying data corresponding to each time interval before the current time interval of the target subway line in parallel into the deep convolutional network model, and executing the deep convolutional network model to obtain the number of passengers carried by the target subway line in the current time interval output by the deep convolutional network model; an allocation execution device connected to the directional identification device, and used to determine the number of emergency medical equipment required by the target subway line in the current time interval based on the number of passengers carried by the target subway line in the current time interval, wherein the determined number of emergency medical equipment required by the target subway line in the current time interval is positively correlated with the number of passengers carried by the target subway line in the current time interval; Wherein, determining the number of emergency medical equipment required by the target subway line in the current time interval based on the number of passengers carried by the target subway line in the current time interval, and the number of emergency medical equipment required by the target subway line in the current time interval being positively associated with the number of passengers carried by the target subway line in the current time interval includes: before the current time interval arrives, executing the allocation of the number of emergency medical equipment required by the target subway line in the current time interval to the target subway line; Among them, each set of transportation data corresponding to each time interval before the current time interval of the target subway line is obtained, and the single set of transportation data corresponding to each time interval is the number of passengers carried by the target subway line in the time interval, including: each time interval before the current time interval and the current time interval form a complete time segment on the time axis and the interval time length of each time interval is equal.

2. The medical health intelligent rescue management system according to claim 1, characterized in that: The object parsing device is used to parse a deep convolutional network model, in which the deep convolutional network is learned multiple times to obtain a deep convolutional network after completing multiple learnings and as the deep convolutional network model, including: the number of times the deep convolutional network is learned is proportional to the total length of the target subway line; The time intervals before the current time interval and the current time interval form a complete time segment on the time axis and the interval time length of each time interval is equal, including: the interval time length of each time interval is greater than 1 day.

3. The medical health intelligent rescue management system according to claim 2, characterized in that: The system further comprises: An information capture component is connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, respectively, and is used to measure the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively; Among them, the information capture component is respectively connected to the directional identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device, and is used to respectively measure the real-time length values ​​of the directional identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device. The information capture component includes multiple length measurement units, which are respectively connected to the directional identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device to complete the respective measurements of the real-time length values ​​of the directional identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device.

4. The medical health intelligent rescue management system as claimed in claim 3, characterized in that: The information capture component includes a plurality of length measurement units, which are used to respectively connect with the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device to complete the respective measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device. The plurality of length measurement units are a plurality of length sensors, which are used to respectively connect with the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device to complete the respective measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object resolution device.

5. The medical health intelligent rescue management system as claimed in claim 4, characterized in that: The multiple length measurement units are multiple length sensors, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, so as to complete the measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, including: the structures of the multiple length sensors are the same.

6. The medical health intelligent rescue management system according to claim 5, characterized in that: The multiple length measurement units are multiple length sensors, which are used to be connected to the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively to complete the measurement of the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device respectively, and also include: the multiple length sensors have the same length measurement upper limit value and length measurement lower limit value.

7. The medical health intelligent rescue management system according to claim 3, characterized in that: The system further comprises: The data display component is respectively connected to the multiple length measurement units of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, and is used to synchronously display the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device.

8. The medical health intelligent rescue management system according to claim 7, characterized in that: A data display component is respectively connected to the directional identification device, the first acquisition mechanism, the second acquisition mechanism and multiple length measurement units of the object analysis device, and is used to synchronously display the real-time length values ​​of the directional identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device. The data display component includes: the data display component is a touch display screen.

9. The medical health intelligent rescue management system according to claim 7, characterized in that: A data display component is respectively connected to the multiple length measurement units of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device, and is used to synchronously display the real-time length values ​​of the orientation identification device, the first acquisition mechanism, the second acquisition mechanism and the object analysis device. The data display component includes: the data display component is a non-touch display screen or an LCD display array.

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