Emergency ambulance transfer assisting method and system based on Internet of Things

Abstract

The invention relates to the field of the Internet of Things, and provides an emergency ambulance transfer assisting method and system based on the Internet of Things, a transfer target is detected and positioned in real time through multi-source data fusion, the error probability is reduced, a data basis is provided for efficient transfer, a first-aid decision model is designed, data analysis is carried out through multimode parallel reasoning, and the accuracy of transfer is improved. The first-aid demand of a transfer target is accurately predicted, various complex transfer conditions are efficiently dealt with so as to adapt to various complex scenes, a transfer path is set and optimized by combining an information management interface, an internet-of-vehicles port and an internet-of-things traffic node, the transfer efficiency is further improved, and a remote collaborative network is constructed, so that the transfer efficiency is improved. Remote direct connection between the transfer receiving landmark and the emergency ambulance terminal is achieved, the state of the transfer target is monitored in real time, emergency allocation is carried out according to the change of the state of the transfer target, and the transfer efficiency and adaptability of the emergency ambulance are improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things, and in particular to an emergency vehicle transport auxiliary method and system based on Internet of Things. Background Art

[0002] With the rapid development of the emergency support system, the comprehensive capability requirements of ambulances, as one of the most critical means of transportation in the emergency support system, are gradually increasing, especially for high efficiency in transportation and the ability to cope with complex scenarios.

[0003] In the prior art, ambulances often serve as independent transfer units, and adopt an emergency support system architecture of a single fixed dispatch center plus multiple independent transfer units. The dispatch center sends and receives instructions, and the ambulance operates according to the instructions of the dispatch center. Due to the communication time difference in dispatch, this architecture leads to the inability to obtain dispatch instructions in time for various situations that arise during transfer, resulting in the transfer efficiency being difficult to meet the requirements. Secondly, because the situations inside and outside the vehicle are complex and changeable, it is often difficult for the dispatch center to accurately command multiple independent transfer units at the same time within the tight transfer time, and the ability to adapt to complex emergency support scenarios is insufficient, and the processing efficiency is further reduced.

[0004] Therefore, how to design an ambulance transfer assistance method to improve the ambulance transfer efficiency and adaptability to complex scenarios has become an urgent problem to be solved. Summary of the invention

[0005] Based on this, the present invention proposes an ambulance transfer auxiliary method and system based on the Internet of Things, which detects and locates the transfer target in real time through multi-source data fusion, reduces the error probability, and provides a data basis for efficient transfer. Then, by designing an emergency decision-making model, data analysis is performed through multi-mode parallel reasoning to accurately predict the emergency needs of the transfer target, and efficient response to various complex transfer situations is achieved, thereby adapting to various complex scenarios. Then, by combining the information management interface, the Internet of Vehicles port and the Internet of Things traffic node, the setting and optimization of the transfer path are realized, and the transfer efficiency is further improved. By constructing a remote collaborative network, a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized, and the transfer target status is monitored in real time to perform emergency deployment according to the change of the transfer target status, further improving the transfer efficiency of the ambulance terminal and the adaptability to complex scenarios. The present invention improves the efficiency and adaptability of ambulance transfer.

[0006] The present invention proposes an emergency vehicle transport assistance method based on the Internet of Things, comprising: Acquire transfer target information and real-time traffic information based on multi-source data fusion; Performing a transfer demand analysis according to the transfer target information to generate a transfer demand report, wherein the transfer demand analysis is based on a first aid decision model, and the first aid decision model includes a multi-mode parallel reasoning architecture; Screening a transfer receiving landmark according to the real-time traffic information and the transfer demand report to generate a transfer path, wherein the transfer path is generated based on an information management interface, a vehicle networking port, and an Internet of Things traffic node; Monitor the transfer target in real time based on the remote collaborative network to obtain information on changes in the transfer target status; Based on the transfer target status change information, emergency dispatch suggestions are sent to the transfer receiving landmark.

[0007] In summary, according to the above-mentioned ambulance transfer auxiliary method based on the Internet of Things, the transfer target is detected and positioned in real time through multi-source data fusion, which reduces the error probability and provides a data basis for efficient transfer. Then, by designing an emergency decision-making model, data analysis is performed through multi-mode parallel reasoning to accurately predict the emergency needs of the transfer target, and efficient response to various complex transfer situations is achieved, thereby adapting to various complex scenarios. Then, by combining the information management interface, the Internet of Vehicles port and the Internet of Things traffic node, the setting and optimization of the transfer path are realized, and the transfer efficiency is further improved. By constructing a remote collaborative network, a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized, and the transfer target status is monitored in real time to perform emergency deployment according to the change of the transfer target status, further improving the transfer efficiency of the ambulance terminal and the adaptability to complex scenarios. The present invention improves the efficiency and adaptability of ambulance transfer. Specifically, based on multi-source data fusion, transfer target information and real-time traffic information are obtained. The error rate of data is reduced through multi-source data fusion, the influence of fluctuating data in complex scenarios is avoided, and a data basis is provided for efficient transfer. According to the transfer target information, a transfer demand analysis is performed to generate a transfer demand report. The transfer demand analysis is based on an emergency decision model. The emergency decision model includes a multi-mode parallel reasoning architecture, which accurately predicts the emergency demand of the transfer target, and realizes efficient response to various complex transfer situations, thereby adapting to various complex scenarios. According to the real-time traffic information and the transfer demand report, a transfer receiving landmark is screened to generate a transfer path. The transfer path is generated based on an information management interface, a vehicle network port, and an Internet of Things traffic node, so that the transfer path is optimized and the transfer efficiency is further improved. According to a remote collaborative network, the transfer target is monitored in real time to obtain transfer target state change information, and a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized to monitor the state change of the transfer target in real time, thereby responding to sudden states during the transfer process and improving the adaptability to complex scenarios. According to the transfer target state change information, an emergency deployment suggestion is sent to the transfer receiving landmark. The present invention improves the efficiency and adaptability of ambulance transfer.

[0008] Furthermore, the step of acquiring the transfer target information and real-time traffic information based on multi-source data fusion specifically includes: The multi-source sensor acquires multi-source data of the transfer target, wherein the multi-source data includes physiological parameters of the transfer target and comprehensive parameters of the transfer target, and the comprehensive parameters of the transfer target are used to identify the identity of the transfer target and locate the position of the transfer target; Perform multi-source data fusion on the transport target physiological parameters and the transport target comprehensive parameters, wherein the multi-source data fusion is based on a filtering fusion algorithm, and error initialization adjustment is performed on the transport target physiological parameters and the transport target comprehensive parameters to obtain initial multi-source fusion data; Performing gain calibration on the initial multi-source fusion data, and if the initial multi-source fusion data exceeds the prediction deviation value, performing weight reduction calibration according to the error dynamic weight, wherein the error dynamic weight is based on the historical error rate; Based on the initial multi-source fusion data after gain calibration, the transfer target information and real-time traffic information are obtained.

[0009] Furthermore, the step of performing a transshipment demand analysis according to the transshipment target information to generate a transshipment demand report specifically includes: Searching a preset historical emergency case database according to the transfer target information to obtain historical emergency case data, wherein the historical emergency case data includes vital sign time series data, transfer time series data, and emergency equipment demand data; A decision analysis is performed on the transfer target information and the historical emergency case data according to the emergency decision model to generate a transfer demand report.

[0010] Furthermore, the step of performing decision analysis on the transfer target information and the historical emergency case data according to the emergency decision model specifically includes: The emergency decision model constructs an input vector according to the transfer target information and the historical emergency case data, so as to perform multi-mode parallel reasoning according to the input vector; The federated learning branch updates the local model of the historical emergency case data, homomorphically encrypts the local model gradient and uploads it to the cloud center. The cloud center performs weighted average aggregation processing based on the historical emergency case data, synchronizes data for all ambulance terminals, transfer reception landmarks, and dispatch command centers to update the global model. The time series branch performs time series data slicing on the transfer target information to obtain multiple time series data features, and performs real-time prediction on the time series data features according to the long short-term memory algorithm to obtain a decision probability prediction; The decision probability prediction is evaluated for value, and the probability distribution weight is dynamically adjusted to obtain the decision suggestion probability distribution.

[0011] Furthermore, the step of screening the transfer receiving landmarks according to the real-time traffic information and the transfer demand report to generate a transfer route specifically includes: Calling the information management interface of different transfer receiving landmarks, and calculating the readiness score of each transfer receiving landmark according to the transfer demand report, wherein the readiness score is based on the landmark equipment availability value and the landmark personnel on-duty value; If it is determined that the readiness score of the transfer receiving landmark is greater than or equal to the readiness requirement threshold in the transfer requirement report, a traffic node road condition calculation is performed based on real-time traffic information. The traffic node road condition calculation is based on the vehicle network port and calculates the estimated travel time and road condition priority of each traffic node; The transfer receiving landmarks are screened according to the estimated travel time, and the transfer route is generated according to the road condition priority. After each traffic node in the transfer route is passed, the congestion coefficient is calculated to dynamically optimize the transfer route.

[0012] Furthermore, the step of monitoring the transfer target in real time according to the remote collaborative network to obtain the transfer target state change information specifically includes: Building a remote collaborative connection based on the remote collaborative network and the transport receiving landmarks to detect the shared transport target physiological parameters in real time based on the physiological indicator sensors; Then, the posture features of the transfer target are acquired in real time, and abnormal state recognition is performed according to the posture features of the transfer target to acquire the state change information of the transfer target; A graded alarm judgment is performed based on the transfer target status change information.

[0013] Furthermore, the step of performing graded alarm judgment according to the transfer target state change information further includes: Performing hierarchical alarm judgment according to the state change information of the transport target to obtain a state deterioration prediction, wherein the hierarchical alarm judgment is based on context features and long-term and short-term time series features in the state change information of the transport target; Demand quantification processing is performed based on the state deterioration prediction to obtain a reception demand change report, and emergency resource allocation analysis is performed based on the reception demand change report to generate emergency allocation suggestions. The emergency resource allocation analysis is based on the emergency resource information of the current transfer receiving landmark and the allocation capacity information of adjacent transfer receiving landmarks.

[0014] The present invention proposes an emergency vehicle transport auxiliary system based on the Internet of Things, comprising: Information acquisition module, used to obtain transfer target information and real-time traffic information based on multi-source data fusion; A demand analysis module, used for performing a transport demand analysis according to the transport target information to generate a transport demand report, wherein the transport demand analysis is based on a first aid decision model, and the first aid decision model includes a multi-mode parallel reasoning architecture; A path generation module, used for screening the transfer receiving landmarks according to the real-time traffic information and the transfer demand report to generate a transfer path, wherein the transfer path is generated based on the information management interface, the vehicle networking port and the Internet of Things traffic node; A remote collaboration module is used to monitor the transfer target in real time based on the remote collaboration network to obtain information on changes in the status of the transfer target; The deployment suggestion module is used to send emergency deployment suggestions to the transfer receiving landmark according to the transfer target state change information.

[0015] The present invention also provides a storage medium, which stores one or more programs, and when the programs are executed by a processor, the ambulance transport assistance method based on the Internet of Things as described above is implemented.

[0016] The present invention also provides a computer device, the computer device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, the above-mentioned emergency vehicle transport assistance method based on the Internet of Things is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of an emergency vehicle transport assistance method based on the Internet of Things proposed in the first embodiment of the present invention; Figure 2 This is a flow chart of an emergency vehicle transport assistance method based on the Internet of Things proposed in the second embodiment of the present invention; Figure 3 This is a schematic structural diagram of an emergency vehicle transport assistance system based on the Internet of Things proposed in the third embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] See also Figure 1 , which is a flow chart of an emergency vehicle transport assistance method based on the Internet of Things proposed in the first embodiment of the present invention, the emergency vehicle transport assistance method based on the Internet of Things includes steps S01 to S05, wherein: Step S01: Acquire transfer target information and real-time traffic information based on multi-source data fusion; It should be noted that in this embodiment, the multi-source sensor acquires multi-source data of the transfer target, and the multi-source data includes physiological parameters of the transfer target and comprehensive parameters of the transfer target, and the comprehensive parameters of the transfer target are used to identify the identity of the transfer target and locate the position of the transfer target; Perform multi-source data fusion on the transport target physiological parameters and the transport target comprehensive parameters, wherein the multi-source data fusion is based on a filtering fusion algorithm, and error initialization adjustment is performed on the transport target physiological parameters and the transport target comprehensive parameters to obtain initial multi-source fusion data; Performing gain calibration on the initial multi-source fusion data, and if the initial multi-source fusion data exceeds the prediction deviation value, performing weight reduction calibration according to the error dynamic weight, wherein the error dynamic weight is based on the historical error rate; Based on the initial multi-source fusion data after gain calibration, the transfer target information and real-time traffic information are obtained.

[0023] Step S02: Performing a transshipment demand analysis based on the transshipment target information to generate a transshipment demand report; It should be noted that in this embodiment, the preset historical emergency case database is searched according to the transfer target information to obtain historical emergency case data, and the historical emergency case data includes vital sign time series data, transfer time series data and emergency equipment demand data; Performing decision analysis on the transfer target information and the historical emergency case data according to the emergency decision model to generate a transfer demand report; The specific algorithm of the first aid decision model is as follows: , in, represents the probability distribution of decision recommendations, represents the activation function, represents the total number of feature weights, represents the feature weight ordinal number, represents the hospital feature weight, which is based on the hospital bed level and emergency response efficiency, represents the balance factor, represents the federated learning function, represents the global weighted average aggregation output, represents the input vector, represents a time series function, represents the long short-term memory output, represents the reinforcement learning coefficient, represents the value assessment function, Indicates the state of the transfer target. Indicates the transfer target demand action, Indicates timing; The emergency decision model constructs an input vector according to the transfer target information and the historical emergency case data, so as to perform multi-mode parallel reasoning according to the input vector; The federated learning branch updates the local model of the historical emergency case data, homomorphically encrypts the local model gradient and uploads it to the cloud center. The cloud center performs weighted average aggregation processing based on the historical emergency case data, synchronizes data for all ambulance terminals, transfer reception landmarks, and dispatch command centers to update the global model. The time series branch performs time series data slicing on the transfer target information to obtain multiple time series data features, and performs real-time prediction on the time series data features according to the long short-term memory algorithm to obtain a decision probability prediction; The decision probability prediction is evaluated for value, and the probability distribution weight is dynamically adjusted to obtain the decision suggestion probability distribution.

[0024] Step S03: Screening transfer receiving landmarks according to real-time traffic information and transfer demand reports to generate transfer routes; It should be noted that in this embodiment, the information management interface of different transfer receiving landmarks is called to calculate the readiness score of each transfer receiving landmark according to the transfer demand report, and the readiness score is based on the landmark equipment vacancy value and the landmark personnel on-duty value; If it is determined that the readiness score of the transfer receiving landmark is greater than or equal to the readiness requirement threshold in the transfer requirement report, a traffic node road condition calculation is performed based on real-time traffic information. The traffic node road condition calculation is based on the vehicle network port and calculates the estimated travel time and road condition priority of each traffic node; The transfer receiving landmarks are screened according to the estimated travel time, and the transfer route is generated according to the road condition priority. After each traffic node in the transfer route is passed, the congestion coefficient is calculated to dynamically optimize the transfer route.

[0025] Step S04: monitoring the transfer target in real time according to the remote collaborative network to obtain the transfer target status change information; It should be noted that in this embodiment, a remote collaborative connection is established based on the remote collaborative network and the transport receiving landmark, so as to detect the shared transport target physiological parameters in real time based on the physiological indicator sensor; Then, the posture features of the transfer target are acquired in real time, and abnormal state recognition is performed according to the posture features of the transfer target to acquire the state change information of the transfer target; A graded alarm judgment is performed based on the transfer target status change information.

[0026] Step S05: sending emergency dispatch suggestions to the transfer receiving landmark according to the transfer target status change information; It should be noted that in this embodiment, a hierarchical alarm judgment is performed according to the state change information of the transport target to obtain a state deterioration prediction, and the hierarchical alarm judgment is based on the context features and long-term and short-term time series features in the state change information of the transport target; Performing demand quantification processing according to the state deterioration prediction to obtain a receiving demand change report, and performing emergency resource allocation analysis according to the receiving demand change report to generate emergency allocation suggestions, wherein the emergency resource allocation analysis is based on the emergency resource information of the current transfer receiving landmark and the allocation capacity information of the adjacent transfer receiving landmarks; The specific algorithm for predicting state deterioration in this embodiment is as follows: , in, represents the predicted probability of deterioration, represents the activation function, Indicates the timing, represents the feature extraction window, represents the total number of feature extraction windows, represents the feature extraction window ordinal number, represents a time series function, Indicates the status change information of the transfer target. Indicates a deteriorated balance parameter.

[0027] In summary, according to the above-mentioned ambulance transfer auxiliary method based on the Internet of Things, the transfer target is detected and positioned in real time through multi-source data fusion, which reduces the error probability and provides a data basis for efficient transfer. Then, by designing an emergency decision-making model, data analysis is performed through multi-mode parallel reasoning to accurately predict the emergency needs of the transfer target, and efficient response to various complex transfer situations is achieved, thereby adapting to various complex scenarios. Then, by combining the information management interface, the Internet of Vehicles port and the Internet of Things traffic node, the setting and optimization of the transfer path are realized, and the transfer efficiency is further improved. By constructing a remote collaborative network, a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized, and the transfer target status is monitored in real time to perform emergency deployment according to the change of the transfer target status, further improving the transfer efficiency of the ambulance terminal and the adaptability to complex scenarios. The present invention improves the efficiency and adaptability of ambulance transfer. Specifically, based on multi-source data fusion, transfer target information and real-time traffic information are obtained. The error rate of data is reduced through multi-source data fusion, the influence of fluctuating data in complex scenarios is avoided, and a data basis is provided for efficient transfer. According to the transfer target information, a transfer demand analysis is performed to generate a transfer demand report. The transfer demand analysis is based on an emergency decision model. The emergency decision model includes a multi-mode parallel reasoning architecture, which accurately predicts the emergency demand of the transfer target, and realizes efficient response to various complex transfer situations, thereby adapting to various complex scenarios. According to the real-time traffic information and the transfer demand report, a transfer receiving landmark is screened to generate a transfer path. The transfer path is generated based on an information management interface, a vehicle network port, and an Internet of Things traffic node, so that the transfer path is optimized and the transfer efficiency is further improved. According to a remote collaborative network, the transfer target is monitored in real time to obtain transfer target state change information, and a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized to monitor the state change of the transfer target in real time, thereby responding to sudden states during the transfer process and improving the adaptability to complex scenarios. According to the transfer target state change information, an emergency deployment suggestion is sent to the transfer receiving landmark. The present invention improves the efficiency and adaptability of ambulance transfer.

[0028] See also Figure 2 , which is a flow chart of an emergency vehicle transport assistance method based on the Internet of Things proposed in the second embodiment of the present invention, the emergency vehicle transport assistance method based on the Internet of Things includes steps S11 to S16, wherein: Step S11: The multi-source sensor acquires multi-source data of the transfer target, performs multi-source data fusion on the transfer target physiological parameters and the transfer target comprehensive parameters, performs error initialization adjustment on the transfer target physiological parameters and the transfer target comprehensive parameters to obtain initial multi-source fusion data, performs gain calibration on the initial multi-source fusion data, and if the initial multi-source fusion data exceeds the predicted deviation value, performs weight reduction calibration according to the error dynamic weight, and obtains the transfer target information and real-time traffic information according to the initial multi-source fusion data after gain calibration; It should be noted that the multi-source data in this embodiment include physiological parameters of the transfer target and comprehensive parameters of the transfer target. The comprehensive parameters of the transfer target are used to identify the identity of the transfer target and locate the position of the transfer target. The multi-source data fusion is based on a filtering fusion algorithm, and the error dynamic weight is based on the historical error rate.

[0029] Step S12: searching a preset historical emergency case database according to the transfer target information to obtain historical emergency case data, and performing decision analysis on the transfer target information and the historical emergency case data according to the emergency decision model to generate a transfer demand report; It should be noted that the historical emergency case data described in this embodiment includes vital signs time series data, transfer time series data and emergency equipment demand data. The preset historical emergency case library is set according to the personal information, physiological state, emergency reason and emergency area of ​​different transfer targets.

[0030] Step S13: The emergency decision model constructs an input vector according to the transfer target information and the historical emergency case data, and performs multi-mode parallel reasoning according to the input vector. The federated learning branch updates the local model of the historical emergency case data, homomorphically encrypts the local model gradient and uploads it to the cloud center. The cloud center performs weighted average aggregation processing according to the historical emergency case data, synchronizes data for all ambulance terminals, transfer receiving landmarks and dispatch command centers to update the global model, and the time series branch performs time series data slicing on the transfer target information to obtain multiple time series data features, and performs real-time prediction of the time series data features according to the long short-term memory algorithm to obtain decision probability prediction, and performs value evaluation on the decision probability prediction, and dynamically adjusts the probability distribution weight to obtain the decision recommendation probability distribution; Step S14: calling the information management interface of different transfer receiving landmarks, calculating the readiness score of each transfer receiving landmark according to the transfer demand report, if it is determined that the readiness score of the transfer receiving landmark is greater than or equal to the readiness demand threshold in the transfer demand report, then calculating the traffic node road condition according to the real-time traffic information, calculating the estimated travel time and road condition priority of each traffic node, screening the transfer receiving landmarks according to the estimated travel time, and generating the transfer path according to the road condition priority, and calculating the congestion coefficient after passing any traffic node in the transfer path, so as to dynamically optimize the transfer path; It should be noted that in this embodiment, the readiness score is based on the landmark equipment availability value and the landmark personnel on-duty value, and the traffic node road condition measurement is based on the vehicle network port.

[0031] Step S15: Building a remote collaborative connection with the transfer receiving landmark according to the remote collaborative network, so as to detect the shared transfer target physiological parameters in real time according to the physiological indicator sensor, and then obtain the transfer target posture characteristics in real time, perform abnormal state recognition according to the transfer target posture characteristics, so as to obtain the transfer target state change information, and perform graded alarm judgment according to the transfer target state change information; Step S16: Performing graded alarm judgment according to the state change information of the transfer target to obtain a state deterioration prediction, performing demand quantification processing according to the state deterioration prediction to obtain a receiving demand change report, and performing emergency resource allocation analysis according to the receiving demand change report to generate emergency allocation suggestions; It should be noted that the hierarchical alarm judgment in this embodiment is based on the contextual features and long- and short-term time series features in the transfer target state change information, and the rescue resource allocation analysis is based on the emergency resource information of the current transfer receiving landmark and the allocation capacity information of the adjacent transfer receiving landmark.

[0032] In summary, according to the above-mentioned ambulance transfer auxiliary method based on the Internet of Things, the transfer target is detected and positioned in real time through multi-source data fusion, which reduces the error probability and provides a data basis for efficient transfer. Then, by designing an emergency decision-making model, data analysis is performed through multi-mode parallel reasoning to accurately predict the emergency needs of the transfer target, and efficient response to various complex transfer situations is achieved, thereby adapting to various complex scenarios. Then, by combining the information management interface, the Internet of Vehicles port and the Internet of Things traffic node, the setting and optimization of the transfer path are realized, and the transfer efficiency is further improved. By constructing a remote collaborative network, a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized, and the transfer target status is monitored in real time to perform emergency deployment according to the change of the transfer target status, further improving the transfer efficiency of the ambulance terminal and the adaptability to complex scenarios. The present invention improves the efficiency and adaptability of ambulance transfer. Specifically, based on multi-source data fusion, transfer target information and real-time traffic information are obtained. The error rate of data is reduced through multi-source data fusion, the influence of fluctuating data in complex scenarios is avoided, and a data basis is provided for efficient transfer. According to the transfer target information, a transfer demand analysis is performed to generate a transfer demand report. The transfer demand analysis is based on an emergency decision model. The emergency decision model includes a multi-mode parallel reasoning architecture, which accurately predicts the emergency demand of the transfer target, and realizes efficient response to various complex transfer situations, thereby adapting to various complex scenarios. According to the real-time traffic information and the transfer demand report, a transfer receiving landmark is screened to generate a transfer path. The transfer path is generated based on an information management interface, a vehicle network port, and an Internet of Things traffic node, so that the transfer path is optimized and the transfer efficiency is further improved. According to a remote collaborative network, the transfer target is monitored in real time to obtain transfer target state change information, and a remote direct connection between the transfer receiving landmark and the ambulance terminal is realized to monitor the state change of the transfer target in real time, thereby responding to sudden states during the transfer process and improving the adaptability to complex scenarios. According to the transfer target state change information, an emergency deployment suggestion is sent to the transfer receiving landmark. The present invention improves the efficiency and adaptability of ambulance transfer.

[0033] See also Figure 3 , which is a schematic diagram of the structure of an emergency vehicle transport auxiliary system based on the Internet of Things proposed in the third embodiment of the present invention, and the system includes: An information acquisition module 10 is used to acquire transfer target information and real-time traffic information based on multi-source data fusion; A demand analysis module 20, configured to perform a transport demand analysis based on the transport target information to generate a transport demand report, wherein the transport demand analysis is based on a first aid decision model, and the first aid decision model includes a multi-mode parallel reasoning architecture; A path generation module 30, for screening the transfer receiving landmarks according to the real-time traffic information and the transfer demand report to generate a transfer path, wherein the transfer path is generated based on the information management interface, the vehicle networking port and the Internet of Things traffic node; The remote collaboration module 40 is used to monitor the transfer target in real time according to the remote collaboration network to obtain the transfer target status change information; The deployment suggestion module 50 is used to send emergency deployment suggestions to the transfer receiving landmark according to the transfer target state change information.

[0034] The present invention also proposes a computer storage medium on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned emergency vehicle transfer assistance method based on the Internet of Things is implemented.

[0035] The present invention also proposes a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the above-mentioned IoT-based ambulance transport assistance method.

[0036] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain storage, communication, propagation or transmission of a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0037] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0038] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0039] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0040] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An emergency vehicle transport assistance method based on the Internet of Things, characterized in that: include: Acquire transfer target information and real-time traffic information based on multi-source data fusion; Performing a transfer demand analysis according to the transfer target information to generate a transfer demand report, wherein the transfer demand analysis is based on a first aid decision model, and the first aid decision model includes a multi-mode parallel reasoning architecture; Screening a transfer receiving landmark according to the real-time traffic information and the transfer demand report to generate a transfer path, wherein the transfer path is generated based on an information management interface, a vehicle networking port, and an Internet of Things traffic node; Monitor the transfer target in real time based on the remote collaborative network to obtain information on changes in the transfer target status; Based on the transfer target status change information, emergency dispatch suggestions are sent to the transfer receiving landmark.

2. The method for assisting emergency vehicle transportation based on the Internet of Things according to claim 1, characterized in that: The step of acquiring transfer target information and real-time traffic information based on multi-source data fusion specifically includes: The multi-source sensor acquires multi-source data of the transfer target, wherein the multi-source data includes physiological parameters of the transfer target and comprehensive parameters of the transfer target, and the comprehensive parameters of the transfer target are used to identify the identity of the transfer target and locate the position of the transfer target; Perform multi-source data fusion on the transport target physiological parameters and the transport target comprehensive parameters, wherein the multi-source data fusion is based on a filtering fusion algorithm, and error initialization adjustment is performed on the transport target physiological parameters and the transport target comprehensive parameters to obtain initial multi-source fusion data; Performing gain calibration on the initial multi-source fusion data, and if the initial multi-source fusion data exceeds the prediction deviation value, performing weight reduction calibration according to the error dynamic weight, wherein the error dynamic weight is based on the historical error rate; Based on the initial multi-source fusion data after gain calibration, the transfer target information and real-time traffic information are obtained.

3. The method for assisting emergency vehicle transportation based on the Internet of Things according to claim 1, characterized in that: The step of performing a transshipment demand analysis according to the transshipment target information to generate a transshipment demand report specifically includes: Searching a preset historical emergency case database according to the transfer target information to obtain historical emergency case data, wherein the historical emergency case data includes vital sign time series data, transfer time series data, and emergency equipment demand data; A decision analysis is performed on the transfer target information and the historical emergency case data according to the emergency decision model to generate a transfer demand report.

4. The method for assisting emergency vehicle transportation based on the Internet of Things according to claim 3, characterized in that: The step of performing decision analysis on the transfer target information and the historical emergency case data according to the emergency decision model specifically includes: The emergency decision model constructs an input vector according to the transfer target information and the historical emergency case data, so as to perform multi-mode parallel reasoning according to the input vector; The federated learning branch updates the local model of the historical emergency case data, homomorphically encrypts the local model gradient and uploads it to the cloud center. The cloud center performs weighted average aggregation processing based on the historical emergency case data, synchronizes data for all ambulance terminals, transfer reception landmarks, and dispatch command centers to update the global model. The time series branch performs time series data slicing on the transfer target information to obtain multiple time series data features, and performs real-time prediction on the time series data features according to the long short-term memory algorithm to obtain a decision probability prediction; The decision probability prediction is evaluated for value, and the probability distribution weight is dynamically adjusted to obtain the decision suggestion probability distribution.

5. The method for assisting emergency vehicle transportation based on the Internet of Things according to claim 1, characterized in that: The step of screening the transfer receiving landmarks according to the real-time traffic information and the transfer demand report to generate a transfer route specifically includes: Calling the information management interface of different transfer receiving landmarks, and calculating the readiness score of each transfer receiving landmark according to the transfer demand report, wherein the readiness score is based on the landmark equipment availability value and the landmark personnel on-duty value; If it is determined that the readiness score of the transfer receiving landmark is greater than or equal to the readiness requirement threshold in the transfer requirement report, a traffic node road condition calculation is performed based on real-time traffic information. The traffic node road condition calculation is based on the vehicle network port and calculates the estimated travel time and road condition priority of each traffic node; The transfer receiving landmarks are screened according to the estimated travel time, and the transfer route is generated according to the road condition priority. After each traffic node in the transfer route is passed, the congestion coefficient is calculated to dynamically optimize the transfer route.

6. The method for assisting emergency vehicle transportation based on the Internet of Things according to claim 1, characterized in that: The step of monitoring the transfer target in real time according to the remote collaborative network to obtain the transfer target state change information specifically includes: Building a remote collaborative connection based on the remote collaborative network and the transport receiving landmarks to detect the shared transport target physiological parameters in real time based on the physiological indicator sensors; Then, the posture features of the transfer target are acquired in real time, and abnormal state recognition is performed according to the posture features of the transfer target to acquire the state change information of the transfer target; A graded alarm judgment is performed based on the transfer target status change information.

7. The method for assisting emergency vehicle transportation based on the Internet of Things according to claim 6, characterized in that: The step of performing graded alarm judgment according to the transfer target state change information further includes: Performing hierarchical alarm judgment according to the state change information of the transport target to obtain a state deterioration prediction, wherein the hierarchical alarm judgment is based on context features and long- and short-term time series features in the state change information of the transport target; Demand quantification processing is performed based on the state deterioration prediction to obtain a reception demand change report, and emergency resource allocation analysis is performed based on the reception demand change report to generate emergency allocation suggestions. The emergency resource allocation analysis is based on the emergency resource information of the current transfer receiving landmark and the allocation capacity information of adjacent transfer receiving landmarks.

8. An emergency vehicle transport assistance system based on the Internet of Things, characterized in that: include: Information acquisition module, used to obtain transfer target information and real-time traffic information based on multi-source data fusion; A demand analysis module, used for performing a transport demand analysis according to the transport target information to generate a transport demand report, wherein the transport demand analysis is based on a first aid decision model, and the first aid decision model includes a multi-mode parallel reasoning architecture; A path generation module, used for screening the transfer receiving landmarks according to the real-time traffic information and the transfer demand report to generate a transfer path, wherein the transfer path is generated based on the information management interface, the vehicle networking port and the Internet of Things traffic node; A remote collaboration module is used to monitor the transfer target in real time based on the remote collaboration network to obtain information on changes in the status of the transfer target; The deployment suggestion module is used to send emergency deployment suggestions to the transfer receiving landmark according to the transfer target state change information.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the IoT-based ambulance transport assistance method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the IoT-based ambulance transport assistance method described in any one of claims 1 to 7.

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