Remote visual cooperative assistance method and system in operation of emergency ambulance

Abstract

The invention relates to the field of cooperative assistance, and provides a remote visual cooperative assistance method and system in the operation of an emergency ambulance, which can accurately position and record the transfer state of an emergency ambulance transfer terminal by enhancing the track of the emergency ambulance transfer terminal so as to provide an accurate scheduling basis. The transfer path is calculated through the urban road network topological graph, so that the transfer path is optimized in real time, the transfer efficiency is improved, end-to-end connection is carried out through the remote collaborative network, efficient communication between the emergency ambulance transfer terminal and the transfer receiving terminal is realized, the transfer target state is accurately judged, and the transfer efficiency is improved. A scheduling model is designed to realize multi-party resource cooperative scheduling, the scheduling command efficiency is improved, auxiliary decision making and risk early warning are performed according to multi-party dynamic information, state change and condition upgrade of an emergency event are predicted, the adaptive capacity for coping with a complex scene is improved, and the emergency command efficiency is improved. According to the invention, the scheduling command efficiency and the adaptive capacity for coping with complex scenes are improved.

Description

Technical Field

[0001] The present invention relates to the field of collaborative assistance, and in particular to a remote visual collaborative assistance method and system in the operation of an emergency vehicle. Background Art

[0002] With the rapid development of the emergency support system, the requirements for emergency support have also increased. In particular, for the dispatching and commanding centers, they need to face complex emergency support scenarios, a large number of ambulance transfer terminals and emergency target receiving terminals. The efficiency and accuracy of dispatching and command is one of the most important factors affecting the emergency support process.

[0003] In the existing technology, collaborative assistance often adopts a dispatching and command architecture in which the dispatching and command center communicates with the ambulance transfer terminal and the emergency target receiving terminal in a single line. The dispatching and command center bears a huge pressure of sending and receiving information. At the same time, the dispatching and command center often uses manual decision-making and analysis, resulting in low dispatching and command efficiency when facing complex emergency support scenarios, and the accuracy of decision-making and early warning deduction is poor, which makes it difficult to meet the increasing requirements of emergency support.

[0004] Therefore, how to design a collaborative assistance method for ambulance operation to improve the efficiency of dispatching and command and the accuracy of decision-making and early warning has become an urgent problem to be solved. Summary of the invention

[0005] Based on this, the present invention proposes a remote visualization collaborative assistance method and system for ambulance operation, which enhances the trajectory of the ambulance transfer terminal to accurately locate and record the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and then calculates the transfer path through the urban road network topology map to optimize the transfer path in real time, thereby improving the efficiency of transfer. It also uses a remote collaborative network for end-to-end connection to achieve efficient communication between the ambulance transfer terminal and the transfer receiving terminal to accurately judge the transfer target status, and designs a scheduling model to perform utility calculation and dynamic priority allocation according to the transfer target status, thereby achieving collaborative dispatch of resources from multiple parties and improving the efficiency of scheduling and command. It then performs auxiliary decision-making and risk warning based on multi-party dynamic information, predicts changes in the state of emergency incidents and escalations of situations, and improves the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios.

[0006] The present invention proposes a remote visualization collaborative assistance method for emergency vehicle operation, comprising: Based on multi-source data fusion, the positioning information of the emergency vehicle transfer terminal is obtained and the trajectory enhancement is performed to obtain the emergency vehicle transfer trajectory, wherein the trajectory enhancement is based on the historical trajectory sequence features extracted from the positioning information; Performing a transfer path optimization calculation based on the ambulance transfer trajectory to obtain an optimized transfer path for the ambulance, wherein the transfer path optimization calculation is performed based on a traversal calculation of a city road network topology map; Perform multi-view fusion monitoring on the transfer target in the ambulance transfer terminal to obtain the transfer target status information, wherein the multi-view fusion monitoring is used to perform regional positioning and coordinate correction on the key areas of the transfer target; Coordinated dispatch of multiple resources according to the transfer target status information to obtain dynamic information of multiple parties, wherein the coordinated dispatch of multiple resources performs utility calculation and dynamic priority allocation based on a scheduling model; Auxiliary decision-making is performed based on the multi-party dynamic information to obtain a risk warning report, and the auxiliary decision-making is used to calculate the risk assessment level.

[0007] In summary, according to the above-mentioned remote visualization collaborative assistance method in the operation of an ambulance, the trajectory of the ambulance transfer terminal is enhanced to accurately locate and record the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and then the transfer path is calculated through the urban road network topology map to optimize the transfer path in real time, thereby improving the efficiency of the transfer. End-to-end connection is performed through a remote collaborative network to achieve efficient communication between the ambulance transfer terminal and the transfer receiving terminal to accurately judge the transfer target status, and a scheduling model is designed to perform utility calculation and dynamic priority allocation according to the transfer target status, thereby achieving collaborative dispatch of resources from multiple parties and improving the efficiency of scheduling and command. Assisted decision-making and risk warning are then performed based on multi-party dynamic information, and predictions are made for changes in the state of emergency incidents and escalations of situations, thereby improving the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios. Specifically, based on multi-source data fusion, the positioning information of the ambulance transfer terminal is obtained and the trajectory is enhanced to obtain the ambulance transfer trajectory. The trajectory enhancement is based on the historical trajectory sequence characteristics extracted from the positioning information, accurately locates and records the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and performs transfer path optimization calculation according to the ambulance transfer trajectory to obtain the optimized transfer path of the ambulance. The transfer path optimization calculation is based on the traversal calculation of the urban road network topology map, optimizes the transfer path in real time, and improves the transfer efficiency. The transfer target in the ambulance transfer terminal is monitored by multi-perspective fusion to obtain the transfer target status information. The multi-perspective fusion monitoring is used to perform regional positioning of key areas of the transfer target. The position and coordinate corrections are realized to achieve end-to-end connection, and efficient communication between the ambulance transfer terminal and the transfer receiving terminal is ensured to accurately judge the transfer target status, and multi-party resources are coordinated and dispatched according to the transfer target status information to obtain multi-party dynamic information. The multi-party resource coordinated dispatch is based on the scheduling model to perform utility calculation and dynamic priority allocation, thereby realizing multi-party resource coordinated dispatch and improving the efficiency of scheduling and command. Auxiliary decision-making is performed according to the multi-party dynamic information to obtain risk warning reports. The auxiliary decision-making is used to calculate the risk assessment level, predict the changes in the state of emergency incidents and the escalation of the situation, and improve the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios.

[0008] Furthermore, the step of obtaining the positioning information of the emergency vehicle transfer terminal based on multi-source data fusion and performing trajectory enhancement to obtain the emergency vehicle transfer trajectory specifically includes: Obtain the onboard navigation coordinate data of the emergency vehicle transfer terminal and the coordinate data of the IoT base station; Perform multi-source data fusion on the vehicle navigation coordinate data and the Internet of Things base station coordinate data according to a filtering fusion algorithm to obtain multi-source fusion data; Extracting features from the multi-source fusion data in a time-series order to obtain the movement features of the emergency vehicle and the historical trajectory sequence features; Performing motion prediction on the motion characteristics of the ambulance according to the long short-term memory network to obtain a predicted moving distance value of the ambulance transfer terminal, and then performing trajectory prediction according to the historical trajectory sequence characteristics and the predicted moving distance value to obtain a predicted trajectory; The trajectory of the ambulance transfer terminal is enhanced according to the predicted trajectory to obtain the ambulance transfer trajectory.

[0009] Furthermore, the step of performing transfer path optimization calculation according to the ambulance transfer trajectory to obtain the optimized transfer path of the ambulance specifically includes: Constructing an urban road network topology map of the current transfer area according to the positioning information of the ambulance transfer terminal, wherein the nodes of the urban road network topology map are traffic intersection nodes, and obtaining a real-time congestion coefficient and an accident risk coefficient according to the urban road network topology map; Perform weight calculation based on the real-time congestion coefficient and the accident risk coefficient to obtain a path risk weight value; The adjacent nodes of the urban road network topology map are traversed according to the path risk weight value to obtain the optimized transfer path for the ambulance.

[0010] Furthermore, the step of performing multi-view fusion monitoring on the transfer target in the ambulance transfer terminal to obtain the transfer target status information specifically includes: Perform multi-view video acquisition on the transport target in the ambulance transport terminal to obtain multi-view video data; Building a remote collaborative network between the emergency vehicle transfer terminal and the transfer receiving terminal, and performing visual sharing processing on the multi-view video data, wherein the visual sharing processing includes split-screen image data display; Performing feature enhancement processing on the multi-view video data according to the multi-head attention mechanism to identify the key areas of the transfer target, and performing remote expert-assisted labeling on the transfer receiving end to obtain basic positioning information of the key areas; The transmission delay information is detected in real time to correct the image area coordinates of the key area basic positioning information according to the transmission delay information to obtain the transfer target status information.

[0011] Furthermore, the step of performing coordinated dispatch of multiple resources according to the transfer target status information to obtain dynamic information of multiple parties specifically includes: According to the transfer target status information, information is matched in the preset historical emergency case database to obtain the transfer target resource demand information; Acquire multi-party resource status information to build a scheduling model according to the multi-party resource status information and the target resource demand information, wherein the scheduling model calculates multi-party utility values ​​based on the multi-party resource status information to determine whether the multi-party utility values ​​meet the target resource demand information; If it is determined that there is a unilateral utility value that does not meet the target resource demand information among the multi-party utility values, the priority allocation is dynamically adjusted to obtain multi-party dynamic information.

[0012] Furthermore, the step of performing auxiliary decision-making based on the multi-party dynamic information to obtain a risk warning report specifically includes: Build auxiliary decision tree models based on multi-party dynamic information to obtain key dynamic features; Generate an auxiliary decision branch according to the key dynamic feature, wherein the auxiliary decision branch is used to calculate a decision gain value; Performing reinforcement learning processing on the decision gain value to generate a decision dynamic weight; The decision is optimized and updated according to the dynamic decision weights to obtain a risk warning report.

[0013] Furthermore, the step of optimizing and updating the decision according to the decision dynamic weight and obtaining the risk warning report specifically includes: Obtaining statistical data and a dynamic timeline of current emergency support events, and performing multidimensional risk calculations based on decision-making dynamic weights to obtain multidimensional risk assessment scores, wherein the multidimensional risk calculations are based on a weighted risk assessment model; Classify the risk warning level according to the multi-dimensional risk assessment score to obtain the risk assessment level of the current emergency support event; A risk warning report is generated according to the risk assessment level, so as to carry out multi-party linkage warning based on the remote collaborative network.

[0014] The present invention proposes a remote visualization collaborative assistance system for emergency vehicle operation, comprising: A trajectory enhancement module is used to obtain the positioning information of the emergency vehicle transfer terminal based on multi-source data fusion and perform trajectory enhancement to obtain the emergency vehicle transfer trajectory, wherein the trajectory enhancement is based on historical trajectory sequence features extracted from the positioning information; A path optimization module is used to perform a transfer path optimization calculation based on the emergency vehicle transfer trajectory to obtain an optimized transfer path for the emergency vehicle, wherein the transfer path optimization calculation is performed based on a traversal calculation of a city road network topology map; A multi-view fusion detection module is used to perform multi-view fusion monitoring on the transfer target in the emergency vehicle transfer terminal to obtain the transfer target status information. The multi-view fusion monitoring is used to perform regional positioning and coordinate correction on the key areas of the transfer target; A collaborative dispatching module, used for performing collaborative dispatching of multiple resources according to the transport target status information to obtain dynamic information of multiple parties, wherein the collaborative dispatching of multiple resources performs utility calculation and dynamic priority allocation based on a scheduling model; The decision warning module is used to make auxiliary decisions based on the multi-party dynamic information to obtain a risk warning report, and the auxiliary decision is used to calculate the risk assessment level.

[0015] The present invention also provides a storage medium, wherein the storage medium stores one or more programs, and when the programs are executed by a processor, the remote visualization collaborative assistance method in the operation of an emergency vehicle 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 remote visualization collaborative assistance method in the operation of the emergency vehicle as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a remote visualization collaborative assistance method for emergency vehicle operation proposed in the first embodiment of the present invention; Figure 2 A flowchart of a remote visual collaborative assistance method for emergency vehicle operation proposed in a second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a remote visualization collaborative assistance system for emergency vehicle operation 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 a remote visualization collaborative assistance method for emergency vehicle operation proposed in the first embodiment of the present invention, wherein the remote visualization collaborative assistance method for emergency vehicle operation comprises steps S01 to S05, wherein: Step S01: Acquire the positioning information of the emergency vehicle transfer terminal based on multi-source data fusion and perform trajectory enhancement to obtain the emergency vehicle transfer trajectory; It should be noted that in this embodiment, the vehicle navigation coordinate data of the emergency vehicle transfer terminal and the Internet of Things base station coordinate data are obtained; Perform multi-source data fusion on the vehicle navigation coordinate data and the Internet of Things base station coordinate data according to a filtering fusion algorithm to obtain multi-source fusion data; Extracting features from the multi-source fusion data in a time-series order to obtain the movement features of the emergency vehicle and the historical trajectory sequence features; Performing motion prediction on the motion characteristics of the ambulance according to the long short-term memory network to obtain a predicted moving distance value of the ambulance transfer terminal, and then performing trajectory prediction according to the historical trajectory sequence characteristics and the predicted moving distance value to obtain a predicted trajectory; The trajectory of the ambulance transfer terminal is enhanced according to the predicted trajectory to obtain the ambulance transfer trajectory.

[0023] Step S02: Optimizing the transfer path according to the transfer trajectory of the ambulance to obtain the optimized transfer path of the ambulance; It should be noted that in this embodiment, an urban road network topology map of the current transfer area is constructed based on the positioning information of the ambulance transfer terminal, and the nodes of the urban road network topology map are traffic intersection nodes. The real-time congestion coefficient and accident risk coefficient are obtained based on the urban road network topology map; Perform weight calculation according to the real-time congestion coefficient and accident risk coefficient to obtain a path risk weight value; The adjacent nodes of the urban road network topology graph are traversed according to the path risk weight value to obtain an optimized transfer path for the ambulance.

[0024] Step S03: Perform multi-view fusion monitoring on the transfer target in the ambulance transfer terminal to obtain transfer target status information; It should be noted that in this embodiment, multi-view video acquisition is performed on the transfer target in the ambulance transfer terminal to obtain multi-view video data; Building a remote collaborative network between the emergency vehicle transfer terminal and the transfer receiving terminal, and performing visual sharing processing on the multi-view video data, wherein the visual sharing processing includes split-screen image data display; Performing feature enhancement processing on the multi-view video data according to the multi-head attention mechanism to identify the key areas of the transfer target, and performing remote expert-assisted labeling on the transfer receiving end to obtain basic positioning information of the key areas; The transmission delay information is detected in real time to correct the image area coordinates of the key area basic positioning information according to the transmission delay information to obtain the transfer target status information.

[0025] Step S04: Coordinated dispatch of multiple resources according to the transfer target status information to obtain dynamic information from multiple parties; It should be noted that in this embodiment, information matching is performed in a preset historical emergency case database according to the transfer target status information to obtain the transfer target resource demand information; Acquire multi-party resource status information to build a scheduling model according to the multi-party resource status information and the target resource demand information, wherein the scheduling model calculates multi-party utility values ​​based on the multi-party resource status information to determine whether the multi-party utility values ​​meet the target resource demand information; The scheduling model in this embodiment is as follows: , in, represents the multi-party maximization utility, represents the maximization function, represents the total number of parties involved in the scheduling, Represents any single party among the multiple parties involved in the scheduling, Indicates the priority weight of the corresponding party, represents the utility function corresponding to one party, represents the delay penalty coefficient. In this embodiment =0.7, Indicates scheduling delay; If it is determined that there is a unilateral utility value that does not meet the target resource demand information among the multi-party utility values, the priority allocation is dynamically adjusted to obtain multi-party dynamic information.

[0026] Step S05: Assist decision making based on dynamic information from multiple parties to obtain a risk warning report; It should be noted that in this embodiment, an auxiliary decision tree model is constructed based on multi-party dynamic information to obtain key dynamic features; Generate an auxiliary decision branch according to the key dynamic feature, wherein the auxiliary decision branch is used to calculate a decision gain value; Performing reinforcement learning processing on the decision gain value to generate a decision dynamic weight; Optimize and update the decision according to the dynamic decision weights to obtain a risk warning report; Obtaining statistical data and a dynamic timeline of current emergency support events, and performing multidimensional risk calculations based on decision-making dynamic weights to obtain multidimensional risk assessment scores, wherein the multidimensional risk calculations are based on a weighted risk assessment model; The specific algorithm of the weighted risk assessment model is as follows: , in, represents the multidimensional risk assessment score, Indicates the total number of emergency support incidents. Indicates the ordinal number of the emergency support event. Represents the weight of emergency support events, Indicates the frequency of events, represents the risk weight, Represents multi-dimensional risk, and the risk weight is set based on the hospital information expert system, which is 0.8 in this embodiment; Classify the risk warning level according to the multi-dimensional risk assessment score to obtain the risk assessment level of the current emergency support event; In this embodiment, when the multi-dimensional risk assessment score is less than 40, the risk warning level is divided into level 1; when the multi-dimensional risk assessment score is greater than or equal to 40 and less than 70, the risk warning level is divided into level 2; when the multi-dimensional risk assessment score is greater than or equal to 70, the risk warning level is divided into level 3; A risk warning report is generated according to the risk assessment level, so as to carry out multi-party linkage warning based on the remote collaborative network.

[0027] In summary, according to the above-mentioned remote visualization collaborative assistance method in the operation of an ambulance, the trajectory of the ambulance transfer terminal is enhanced to accurately locate and record the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and then the transfer path is calculated through the urban road network topology map to optimize the transfer path in real time, thereby improving the efficiency of the transfer. End-to-end connection is performed through a remote collaborative network to achieve efficient communication between the ambulance transfer terminal and the transfer receiving terminal to accurately judge the transfer target status, and a scheduling model is designed to perform utility calculation and dynamic priority allocation according to the transfer target status, thereby achieving collaborative dispatch of resources from multiple parties and improving the efficiency of scheduling and command. Assisted decision-making and risk warning are then performed based on multi-party dynamic information, and predictions are made for changes in the state of emergency incidents and escalations of situations, thereby improving the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios. Specifically, based on multi-source data fusion, the positioning information of the ambulance transfer terminal is obtained and the trajectory is enhanced to obtain the ambulance transfer trajectory. The trajectory enhancement is based on the historical trajectory sequence characteristics extracted from the positioning information, accurately locates and records the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and performs transfer path optimization calculation according to the ambulance transfer trajectory to obtain the optimized transfer path of the ambulance. The transfer path optimization calculation is based on the traversal calculation of the urban road network topology map, optimizes the transfer path in real time, and improves the transfer efficiency. The transfer target in the ambulance transfer terminal is monitored by multi-perspective fusion to obtain the transfer target status information. The multi-perspective fusion monitoring is used to perform regional positioning of key areas of the transfer target. The position and coordinate corrections are realized to achieve end-to-end connection, and efficient communication between the ambulance transfer terminal and the transfer receiving terminal is ensured to accurately judge the transfer target status, and multi-party resources are coordinated and dispatched according to the transfer target status information to obtain multi-party dynamic information. The multi-party resource coordinated dispatch is based on the scheduling model to perform utility calculation and dynamic priority allocation, thereby realizing multi-party resource coordinated dispatch and improving the efficiency of scheduling and command. Auxiliary decision-making is performed according to the multi-party dynamic information to obtain risk warning reports. The auxiliary decision-making is used to calculate the risk assessment level, predict the changes in the state of emergency incidents and the escalation of the situation, and improve the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios.

[0028] See also Figure 2 , which is a flow chart of a remote visualization collaborative assistance method for emergency vehicle operation proposed in a second embodiment of the present invention, wherein the remote visualization collaborative assistance method for emergency vehicle operation comprises steps S11 to S16, wherein: Step S11: obtaining the on-board navigation coordinate data and the Internet of Things base station coordinate data of the ambulance transfer terminal, performing multi-source data fusion on the on-board navigation coordinate data and the Internet of Things base station coordinate data according to a filtering fusion algorithm to obtain multi-source fusion data, performing feature extraction on the multi-source fusion data in a time series order to obtain the motion characteristics of the ambulance and the historical trajectory sequence characteristics, performing motion prediction on the motion characteristics of the ambulance according to a long short-term memory network to obtain a moving distance prediction value of the ambulance transfer terminal, then performing trajectory prediction according to the historical trajectory sequence characteristics and the moving distance prediction value to obtain a predicted trajectory, and performing trajectory enhancement on the ambulance transfer terminal according to the predicted trajectory to obtain an ambulance transfer trajectory; Step S12: constructing an urban road network topology map of the current transfer area according to the positioning information of the ambulance transfer terminal, obtaining a real-time congestion coefficient and an accident risk coefficient according to the urban road network topology map, performing weight calculation according to the real-time congestion coefficient and the accident risk coefficient to obtain a path risk weight value, and traversing adjacent nodes of the urban road network topology map according to the path risk weight value to obtain an optimized transfer path for the ambulance; It should be noted that the nodes of the urban road network topology diagram described in this embodiment are traffic intersection nodes.

[0029] Step S13: multi-view video acquisition is performed on the transfer target in the ambulance transfer terminal to obtain multi-view video data, a remote collaborative network is constructed between the ambulance transfer terminal and the transfer receiving terminal, the multi-view video data is visualized and shared, and feature enhancement is performed on the multi-view video data according to the multi-head attention mechanism to identify the key areas of the transfer target. The transfer receiving terminal performs remote expert-assisted labeling to obtain basic positioning information of the key areas, and real-time detection of transmission delay information is performed to correct the image area coordinates of the basic positioning information of the key areas according to the transmission delay information to obtain the transfer target status information; It should be noted that the visualization sharing process described in this embodiment includes split-screen image data display.

[0030] Step S14: matching information in a preset historical emergency case database according to the transfer target status information to obtain transfer target resource demand information, and obtaining multi-party resource status information to build a scheduling model according to the multi-party resource status information and the target resource demand information. The scheduling model calculates multi-party utility values ​​based on the multi-party resource status information to determine whether the multi-party utility values ​​meet the target resource demand information. If it is determined that there is a unilateral utility value in the multi-party utility value that does not meet the target resource demand information, the priority allocation is dynamically adjusted to obtain multi-party dynamic information; Step S15: construct an auxiliary decision tree model based on multi-party dynamic information to obtain key dynamic features, generate auxiliary decision branches according to the key dynamic features, the auxiliary decision branches are used to calculate decision gain values, and the decision gain values ​​are subjected to reinforcement learning processing to generate decision dynamic weights. Decision optimization and update are performed according to the decision dynamic weights to obtain a risk warning report; Step S16: Obtain statistical data and dynamic timeline of the current emergency support event, perform multi-dimensional risk calculation according to the decision dynamic weight to obtain a multi-dimensional risk assessment score, perform risk warning level classification according to the multi-dimensional risk assessment score to obtain the risk assessment level of the current emergency support event, generate a risk warning report according to the risk assessment level, and perform multi-party linkage warning according to the remote collaborative network; It should be noted that the multi-dimensional risk calculation described in this embodiment is based on a weighted risk assessment model.

[0031] In summary, according to the above-mentioned remote visualization collaborative assistance method in the operation of an ambulance, the trajectory of the ambulance transfer terminal is enhanced to accurately locate and record the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and then the transfer path is calculated through the urban road network topology map to optimize the transfer path in real time, thereby improving the efficiency of the transfer. End-to-end connection is performed through a remote collaborative network to achieve efficient communication between the ambulance transfer terminal and the transfer receiving terminal to accurately judge the transfer target status, and a scheduling model is designed to perform utility calculation and dynamic priority allocation according to the transfer target status, thereby achieving collaborative dispatch of resources from multiple parties and improving the efficiency of scheduling and command. Assisted decision-making and risk warning are then performed based on multi-party dynamic information, and predictions are made for changes in the state of emergency incidents and escalations of situations, thereby improving the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios. Specifically, based on multi-source data fusion, the positioning information of the ambulance transfer terminal is obtained and the trajectory is enhanced to obtain the ambulance transfer trajectory. The trajectory enhancement is based on the historical trajectory sequence characteristics extracted from the positioning information, accurately locates and records the transfer status of the ambulance transfer terminal to provide an accurate scheduling basis, and performs transfer path optimization calculation according to the ambulance transfer trajectory to obtain the optimized transfer path of the ambulance. The transfer path optimization calculation is based on the traversal calculation of the urban road network topology map, optimizes the transfer path in real time, and improves the transfer efficiency. The transfer target in the ambulance transfer terminal is monitored by multi-perspective fusion to obtain the transfer target status information. The multi-perspective fusion monitoring is used to perform regional positioning of key areas of the transfer target. The position and coordinate corrections are realized to achieve end-to-end connection, and efficient communication between the ambulance transfer terminal and the transfer receiving terminal is ensured to accurately judge the transfer target status, and multi-party resources are coordinated and dispatched according to the transfer target status information to obtain multi-party dynamic information. The multi-party resource coordinated dispatch is based on the scheduling model to perform utility calculation and dynamic priority allocation, thereby realizing multi-party resource coordinated dispatch and improving the efficiency of scheduling and command. Auxiliary decision-making is performed according to the multi-party dynamic information to obtain risk warning reports. The auxiliary decision-making is used to calculate the risk assessment level, predict the changes in the state of emergency incidents and the escalation of the situation, and improve the adaptability to complex scenarios. The present invention improves the efficiency of scheduling and command and the adaptability to complex scenarios.

[0032] See also Figure 3 , which is a schematic diagram of the structure of a remote visual collaborative assistance system for emergency vehicle operation proposed in the third embodiment of the present invention, and the system includes: The trajectory enhancement module 10 is used to obtain the positioning information of the emergency vehicle transfer terminal based on multi-source data fusion and perform trajectory enhancement when the emergency vehicle is running, so as to obtain the emergency vehicle transfer trajectory, wherein the trajectory enhancement is based on the historical trajectory sequence features extracted from the positioning information; A path optimization module 20 is used to perform a transfer path optimization calculation based on the emergency vehicle transfer trajectory to obtain an optimized transfer path for the emergency vehicle, wherein the transfer path optimization calculation is performed based on a traversal calculation of a city road network topology map; The multi-view fusion detection module 30 is used to perform multi-view fusion monitoring on the transfer target in the emergency vehicle transfer terminal to obtain the transfer target status information, and the multi-view fusion monitoring is used to perform regional positioning and coordinate correction on the key area of ​​the transfer target; A coordinated dispatching module 40 is used to perform coordinated dispatching of multiple resources according to the transport target status information to obtain dynamic information of multiple parties, wherein the coordinated dispatching of multiple resources performs utility calculation and priority dynamic allocation based on a scheduling model; The decision warning module 50 is used to make auxiliary decisions based on the multi-party dynamic information to obtain a risk warning report, and the auxiliary decision is used to calculate the risk assessment level.

[0033] The present invention also proposes a computer storage medium on which one or more programs are stored, and when the programs are executed by a processor, the remote visualization collaborative assistance method in the operation of the emergency vehicle is implemented.

[0034] 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 remote visualization collaborative assistance method in the operation of an ambulance.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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. A remote visualization collaborative assistance method for emergency vehicle operation, characterized in that: include: Based on multi-source data fusion, the positioning information of the emergency vehicle transfer terminal is obtained and the trajectory enhancement is performed to obtain the emergency vehicle transfer trajectory, wherein the trajectory enhancement is based on the historical trajectory sequence features extracted from the positioning information; Performing a transfer path optimization calculation based on the ambulance transfer trajectory to obtain an optimized transfer path for the ambulance, wherein the transfer path optimization calculation is performed based on a traversal calculation of a city road network topology map; Perform multi-view fusion monitoring on the transfer target in the ambulance transfer terminal to obtain the transfer target status information, wherein the multi-view fusion monitoring is used to perform regional positioning and coordinate correction on the key areas of the transfer target; Coordinated dispatch of multiple resources according to the transfer target status information to obtain dynamic information of multiple parties, wherein the coordinated dispatch of multiple resources performs utility calculation and dynamic priority allocation based on a scheduling model; Auxiliary decision-making is performed based on the multi-party dynamic information to obtain a risk warning report, and the auxiliary decision-making is used to calculate the risk assessment level.

2. The remote visualization collaborative assistance method in the operation of an emergency vehicle according to claim 1, characterized in that: The step of acquiring the positioning information of the emergency vehicle transfer terminal based on multi-source data fusion and performing trajectory enhancement to obtain the emergency vehicle transfer trajectory specifically includes: Obtain the onboard navigation coordinate data of the emergency vehicle transfer terminal and the coordinate data of the IoT base station; Perform multi-source data fusion on the vehicle navigation coordinate data and the Internet of Things base station coordinate data according to a filtering fusion algorithm to obtain multi-source fusion data; Extracting features from the multi-source fusion data in a time-series order to obtain the movement features of the emergency vehicle and the historical trajectory sequence features; Performing motion prediction on the motion characteristics of the ambulance according to the long short-term memory network to obtain a predicted moving distance value of the ambulance transfer terminal, and then performing trajectory prediction according to the historical trajectory sequence characteristics and the predicted moving distance value to obtain a predicted trajectory; The trajectory of the ambulance transfer terminal is enhanced according to the predicted trajectory to obtain the ambulance transfer trajectory.

3. The remote visualization collaborative assistance method in the operation of an emergency vehicle according to claim 1, characterized in that: The step of performing transfer path optimization calculation according to the ambulance transfer trajectory to obtain the optimized transfer path for the ambulance specifically includes: Constructing an urban road network topology map of the current transfer area according to the positioning information of the ambulance transfer terminal, wherein the nodes of the urban road network topology map are traffic intersection nodes, and obtaining a real-time congestion coefficient and an accident risk coefficient according to the urban road network topology map; Perform weight calculation according to the real-time congestion coefficient and accident risk coefficient to obtain a path risk weight value; The adjacent nodes of the urban road network topology map are traversed according to the path risk weight value to obtain the optimized transfer path for the ambulance.

4. The remote visualization collaborative assistance method in the operation of an emergency vehicle according to claim 1, characterized in that: The step of performing multi-view fusion monitoring on the transfer target in the ambulance transfer terminal to obtain the transfer target status information specifically includes: Perform multi-view video acquisition on the transport target in the ambulance transport terminal to obtain multi-view video data; Building a remote collaborative network between the emergency vehicle transfer terminal and the transfer receiving terminal, and performing visual sharing processing on the multi-view video data, wherein the visual sharing processing includes split-screen image data display; Performing feature enhancement processing on the multi-view video data according to the multi-head attention mechanism to identify the key areas of the transfer target, and performing remote expert-assisted labeling on the transfer receiving end to obtain basic positioning information of the key areas; The transmission delay information is detected in real time to correct the image area coordinates of the key area basic positioning information according to the transmission delay information to obtain the transfer target status information.

5. The remote visualization collaborative assistance method in the operation of an emergency vehicle according to claim 1, characterized in that: The step of coordinating and dispatching multiple resources according to the transfer target status information to obtain dynamic information from multiple parties specifically includes: According to the transfer target status information, information is matched in the preset historical emergency case database to obtain the transfer target resource demand information; Acquire multi-party resource status information to build a scheduling model according to the multi-party resource status information and the target resource demand information, wherein the scheduling model calculates multi-party utility values ​​based on the multi-party resource status information to determine whether the multi-party utility values ​​meet the target resource demand information; If it is determined that there is a unilateral utility value that does not meet the target resource demand information among the multi-party utility values, the priority allocation is dynamically adjusted to obtain multi-party dynamic information.

6. The remote visualization collaborative assistance method in the operation of an emergency vehicle according to claim 1, characterized in that: The step of performing auxiliary decision-making based on the multi-party dynamic information to obtain a risk warning report specifically includes: Build auxiliary decision tree models based on multi-party dynamic information to obtain key dynamic features; Generate an auxiliary decision branch according to the key dynamic feature, wherein the auxiliary decision branch is used to calculate a decision gain value; Performing reinforcement learning processing on the decision gain value to generate a decision dynamic weight; The decision is optimized and updated according to the dynamic decision weights to obtain a risk warning report.

7. The remote visualization collaborative assistance method in the operation of an emergency vehicle according to claim 6, characterized in that: The step of optimizing and updating the decision according to the decision dynamic weight and obtaining the risk warning report specifically includes: Obtaining statistical data and a dynamic timeline of current emergency support events, and performing multidimensional risk calculations based on decision-making dynamic weights to obtain multidimensional risk assessment scores, wherein the multidimensional risk calculations are based on a weighted risk assessment model; Classify the risk warning level according to the multi-dimensional risk assessment score to obtain the risk assessment level of the current emergency support event; A risk warning report is generated according to the risk assessment level, so as to carry out multi-party linkage warning based on the remote collaborative network.

8. A remote visual collaborative assistance system for emergency vehicles, characterized in that: include: A trajectory enhancement module is used to obtain the positioning information of the emergency vehicle transfer terminal based on multi-source data fusion and perform trajectory enhancement to obtain the emergency vehicle transfer trajectory, wherein the trajectory enhancement is based on historical trajectory sequence features extracted from the positioning information; A path optimization module is used to perform a transfer path optimization calculation based on the emergency vehicle transfer trajectory to obtain an optimized transfer path for the emergency vehicle, wherein the transfer path optimization calculation is performed based on a traversal calculation of a city road network topology map; A multi-view fusion detection module is used to perform multi-view fusion monitoring on the transfer target in the emergency vehicle transfer terminal to obtain the transfer target status information. The multi-view fusion monitoring is used to perform regional positioning and coordinate correction on the key areas of the transfer target; A collaborative dispatching module, used for performing collaborative dispatching of multiple resources according to the transport target status information to obtain dynamic information of multiple parties, wherein the collaborative dispatching of multiple resources performs utility calculation and dynamic priority allocation based on a scheduling model; The decision-making warning module is used to make auxiliary decisions based on the multi-party dynamic information to obtain a risk warning report, and the auxiliary decision is used to calculate the risk assessment level.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the remote visualization collaborative assistance method in the operation of an emergency vehicle 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, the remote visualization collaborative assistance method in the operation of the emergency vehicle as described in any one of claims 1-7 is implemented.

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