Method and System for Transmitting Casualty Sensing Data Based on Clustering and Grouping

Abstract

The present invention discloses a method and system for transmitting casualty sensing data based on clustering grouping. The method includes: obtaining a plurality of injury sensing data to be transmitted of a plurality of casualties in an accident area; determining a plurality of data sets based on a clustering algorithm according to the positions of the casualties and the injury sensing data; determining a transmission device corresponding to each data set according to the data characteristics corresponding to each data set; and sending each data set to the corresponding transmission device for the transmission device to transmit the data set to a cloud device. It can be seen that the present invention can reasonably allocate data transmission resources, reduce delays and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of injury data, and support efficient rescue decision-making and scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for transmitting wounded personnel sensing data based on clustering grouping. Background Art

[0002] In existing accident emergency rescues, it is usually necessary to obtain the injury information of the wounded in real time and transmit the relevant data to cloud devices to assist in rescue decision-making. In the prior art, fixed transmission devices or a method based on a single transmission path are usually used for data transmission. However, in the case of a complex accident environment and uneven distribution of the wounded, the fixed transmission method may lead to low transmission efficiency of some data, and even problems such as data delay or loss. In addition, the prior art lacks a grouping strategy for different data characteristics and cannot optimize the transmission path according to data characteristics, resulting in unbalanced data transmission load and affecting the overall rescue efficiency and timeliness of decision-making. It can be seen that the prior art has defects and urgent solutions are needed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for transmitting wounded personnel sensing data based on clustering grouping, which can reasonably allocate data transmission resources, reduce delays and bandwidth occupancy during data transmission, improve the transmission efficiency and real-time performance of injury data, and support efficient rescue decision-making and scheduling.

[0004] To solve the above technical problem, in a first aspect of the present invention, a method for transmitting wounded personnel sensing data based on clustering grouping is disclosed, and the method includes:

[0005] Obtain a plurality of injury sensing data to be transmitted of a plurality of wounded personnel in the accident area;

[0006] Based on the positions of the wounded personnel and the injury sensing data, determine a plurality of data sets based on a clustering algorithm;

[0007] According to the data characteristics corresponding to each data set, determine a transmission device corresponding to each data set;

[0008] Send each data set to the corresponding transmission device for the transmission device to transmit the data set to the cloud device.

[0009] As an optional implementation manner, in the first aspect of the present invention, the injury sensing data includes at least one of physiological data, motion data, environmental sensing data, image data, and sound data; the physiological data includes at least one of heart rate, blood oxygen saturation, blood pressure, body temperature, respiratory rate, electrocardiogram data, blood glucose level, and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information, and limb mobility; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration, and light intensity.

[0010] As an optional implementation manner, in the first aspect of the present invention, the injury sensing data is obtained through a sensor module in a portable device capable of data transmission on the wounded; the transmission device is the portable device.

[0011] As an optional implementation manner, in the first aspect of the present invention, according to the position of the wounded and the injury sensing data, multiple data sets are determined based on a clustering algorithm, including:

[0012] Based on a position-related rule, according to the position of the wounded, all the injury sensing data is clustered to obtain multiple position data sets; the position data sets include the injury sensing data in which the distance between multiple corresponding wounded positions is less than a preset first distance threshold.

[0013] For each of the position data sets, based on a rule related to injury prediction, all the injury sensing data in the position data set is clustered to obtain at least one injury data set corresponding to the position data set.

[0014] According to at least one injury data set corresponding to each of the position data sets, multiple data sets are determined based on a re-clustering rule.

[0015] As an optional implementation manner, in the first aspect of the present invention, the step of clustering all the injury sensing data in the position data set based on a rule related to injury prediction to obtain at least one injury data set corresponding to the position data set includes:

[0016] Each piece of the injury sensing data in the position data set is input into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each piece of the injury sensing data; the injury prediction neural network is trained through a training data set including multiple training sensing data and corresponding injury annotations.

[0017] Cluster all the injury sensing data according to the injury severity parameter to obtain at least one injury data set corresponding to the location data set; the injury data set includes multiple pieces of injury sensing data whose difference between the corresponding injury severity parameters is less than a preset first difference threshold.

[0018] As an optional implementation manner, in the first aspect of the present invention, determining a plurality of data sets based on the at least one injury data set corresponding to each location data set according to the reclustering rule includes:

[0019] Calculate the average value of the positions of the wounded corresponding to all the injury sensing data in each injury data set to obtain the set position of each injury data set;

[0020] Calculate the average value of the injury severity parameters corresponding to all the injury sensing data in each injury data set to obtain the set injury parameter of each injury data set;

[0021] For injury data sets where the distance between the corresponding set positions of any two is less than a second distance threshold, calculate the parameter difference between the set injury parameters corresponding to the two injury data sets;

[0022] Judge whether the parameter difference is less than a second difference threshold. If so, merge the two injury data sets into one data set; otherwise, keep them as two data sets respectively.

[0023] As an optional implementation manner, in the first aspect of the present invention, determining the transmission device corresponding to each data set according to the data characteristics corresponding to each data set includes:

[0024] For each data set, determine the portable devices of the wounded corresponding to all the injury sensing data in the data set to obtain a plurality of candidate devices;

[0025] Determine the communication stability priority of each candidate device according to the injury sensing data and the position of the wounded corresponding to each candidate device;

[0026] Determine the candidate device with the highest communication stability priority as the transmission device corresponding to the data set.

[0027] As an optional implementation manner, in the first aspect of the present invention, determining the communication stability priority of each candidate device according to the injury sensing data and the position of the wounded corresponding to each candidate device includes:

[0028] Calculate the average value of the position distances between the casualty positions corresponding to each of the candidate devices and the casualty positions of all other candidate devices to obtain the position parameter corresponding to each candidate device;

[0029] Determine the injury severity parameter corresponding to the injury sensing data corresponding to each candidate device;

[0030] Calculate the ratio of the injury severity parameter and the position parameter corresponding to each candidate device to obtain the communication stability priority of each candidate device.

[0031] A second aspect of the embodiments of the present invention discloses a casualty sensing data transmission system based on clustering grouping, and the system includes:

[0032] An acquisition module, configured to acquire a plurality of injury sensing data to be transmitted of a plurality of casualties in an accident area;

[0033] A first determination module, configured to determine a plurality of data sets based on a clustering algorithm according to the positions of the casualties and the injury sensing data;

[0034] A second determination module, configured to determine the transmission device corresponding to each data set according to the data characteristics corresponding to each data set;

[0035] A transmission module, configured to send each data set to the corresponding transmission device for the transmission device to transmit the data set to a cloud device.

[0036] As an optional implementation manner, in the second aspect of the present invention, the injury sensing data includes at least one of physiological data, motion data, environmental sensing data, image data, and sound data; the physiological data includes at least one of heart rate, blood oxygen saturation, blood pressure, body temperature, respiratory rate, electrocardiogram data, blood glucose level, and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information, and limb mobility; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration, and light intensity.

[0037] As an optional implementation manner, in the second aspect of the present invention, the injury sensing data is obtained through a sensor module in a portable device capable of data transmission on the casualty; the transmission device is the portable device.

[0038] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the first determination module determines a plurality of data sets based on a clustering algorithm according to the positions of the casualties and the injury sensing data includes:

[0039] Based on location - related rules, cluster all the injury sensing data according to the location of the casualty to obtain multiple location data sets; the injury sensing data with the distance between multiple corresponding casualty locations less than a preset first distance threshold is included in the location data set.

[0040] For each of the location data sets, based on rules related to injury prediction, cluster all the injury sensing data in the location data set to obtain at least one injury data set corresponding to the location data set.

[0041] Based on the at least one injury data set corresponding to each location data set, determine multiple data sets based on reclustering rules.

[0042] As an alternative implementation, in the second aspect of the present invention, the specific manner in which the first determination module clusters all the injury sensing data in the location data set based on rules related to injury prediction to obtain at least one injury data set corresponding to the location data set includes:

[0043] Input each injury sensing data in the location data set into the trained injury prediction neural network to obtain an injury severity parameter corresponding to each injury sensing data; the injury prediction neural network is trained by a training data set including multiple training sensing data and corresponding injury annotations.

[0044] Cluster all the injury sensing data according to the injury severity parameter to obtain at least one injury data set corresponding to the location data set; the injury data set includes injury sensing data with the difference between multiple corresponding injury severity parameters less than a preset first difference threshold.

[0045] As an alternative implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines multiple data sets based on the at least one injury data set corresponding to each location data set and based on reclustering rules includes:

[0046] Calculate the average value of the casualty locations corresponding to all the injury sensing data in each injury data set to obtain the set location of each injury data set.

[0047] Calculate the average value of the injury severity parameters corresponding to all the injury sensing data in each injury data set to obtain the set injury parameter of each injury data set.

[0048] For the set of injury data where the distance between any two corresponding set positions is less than a second distance threshold, calculate the parameter difference between the set injury parameters corresponding to the two sets of injury data;

[0049] Determine whether the parameter difference is less than a second difference threshold. If so, merge the two sets of injury data into one data set; otherwise, retain them as two separate data sets.

[0050] As an alternative implementation, in the second aspect of the present invention, the second determination module determines the specific method of the transmission device corresponding to each data set according to the data characteristics corresponding to each data set, including:

[0051] For each data set, determine the portable device of the wounded corresponding to all the injury sensing data in the data set to obtain a plurality of candidate devices;

[0052] According to the injury sensing data and the wounded position corresponding to each candidate device, determine the communication stability priority of each candidate device;

[0053] Determine the candidate device with the highest communication stability priority as the transmission device corresponding to the data set.

[0054] As an alternative implementation, in the second aspect of the present invention, the specific method by which the second determination module determines the communication stability priority of each candidate device according to the injury sensing data and the wounded position corresponding to each candidate device includes:

[0055] Calculate the average value of the position distances between the wounded position corresponding to each candidate device and the wounded positions of all other candidate devices to obtain the position parameter corresponding to each candidate device;

[0056] Determine the injury severity parameter corresponding to the injury sensing data corresponding to each candidate device;

[0057] Calculate the ratio of the injury severity parameter and the position parameter corresponding to each candidate device to obtain the communication stability priority of each candidate device.

[0058] The third aspect of the present invention discloses another wounded sensing data transmission system based on clustering grouping. The system includes:

[0059] A memory storing executable program code;

[0060] A processor coupled to the memory;

[0061] The processor calls the executable program code stored in the memory and executes some or all of the steps in the method for transmitting casualty sensing data based on clustering grouping disclosed in the first aspect of the present invention.

[0062] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions that, when called, are used to execute some or all of the steps in the method for transmitting casualty sensing data based on clustering grouping disclosed in the first aspect of the present invention.

[0063] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0064] The present invention can, based on the casualty condition sensing data to be transmitted of multiple casualties in the accident area, use a clustering algorithm to classify this data according to the casualty locations and the casualty condition sensing data, determine the characteristics of each data set, and accordingly match a suitable transmission device, send the data set to the corresponding transmission device, and have it transmit to the cloud device, so as to be able to reasonably allocate data transmission resources, reduce the delay and bandwidth occupancy in the data transmission process, improve the transmission efficiency and real-time performance of the casualty condition data, and support efficient rescue decision-making and dispatching. Description of the Drawings

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0066] Figure 1 It is a flowchart showing the method for transmitting casualty sensing data based on clustering grouping disclosed in the embodiments of the present invention.

[0067] Figure 2 It is a structural diagram showing a system for transmitting casualty sensing data based on clustering grouping disclosed in the embodiments of the present invention.

[0068] Figure 3 It is a structural diagram showing another system for transmitting casualty sensing data based on clustering grouping disclosed in the embodiments of the present invention. Detailed Embodiments

[0069] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0070] The terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0071] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0072] The present invention discloses a method and system for transmitting casualty sensing data based on clustering grouping, which can classify the to-be-transmitted injury sensing data of multiple casualties in the accident area by using a clustering algorithm according to the casualty location and injury sensing data, determine the characteristics of each data set, and accordingly match a suitable transmission device, send the data set to the corresponding transmission device, and transmit it to the cloud device by the transmission device, so as to reasonably allocate data transmission resources, reduce the delay and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of injury data, and support efficient rescue decision-making and scheduling. The following will be described in detail respectively.

[0073] Embodiment 1

[0074] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for transmitting casualty sensing data based on clustering grouping disclosed in an embodiment of the present invention. Among them, Figure 1 the described method for transmitting casualty sensing data based on clustering grouping can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 1As shown in the figure, the method for transmitting casualty sensing data based on clustering grouping may include the following operations:

[0075] 101. Obtain a plurality of injury sensing data to be transmitted of a plurality of casualties in the accident area.

[0076] 102. Based on the positions of the casualties and the injury sensing data, determine a plurality of data sets based on a clustering algorithm.

[0077] 103. Determine the transmission device corresponding to each data set according to the data characteristics corresponding to each data set.

[0078] 104. Send each data set to the corresponding transmission device so that the transmission device transmits the data set to the cloud device.

[0079] It can be seen that the above-mentioned invention embodiments can classify the data based on the injury sensing data to be transmitted of a plurality of casualties in the accident area, use the clustering algorithm according to the positions of the casualties and the injury sensing data, determine the characteristics of each data set, and accordingly match a suitable transmission device, send the data set to the corresponding transmission device, and transmit it to the cloud device by the transmission device, so as to reasonably allocate data transmission resources, reduce the delay and bandwidth occupancy in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0080] As an optional embodiment, in the above steps, the injury sensing data includes at least one of physiological data, motion data, environmental sensing data, image data, and sound data; the physiological data includes at least one of heart rate, blood oxygen saturation, blood pressure, body temperature, respiratory rate, electrocardiogram data, blood glucose level, and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information, and limb mobility; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration, and light intensity.

[0081] It can be seen that through the above optional embodiment, the content of the injury sensing data is defined to comprehensively characterize the characteristics related to the injuries of the casualties, so as to facilitate subsequent data classification and transmission, assist in reasonably allocating data transmission resources, reduce the delay and bandwidth occupancy in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0082] As an optional embodiment, in the above steps, the injury sensing data is obtained through a sensor module in a portable device capable of data transmission on the casualty; the transmission device is a portable device.

[0083] It can be seen that through the above optional embodiments, the acquisition method of injury sensing data is defined, enabling the portable device to simultaneously acquire and transmit injury data, facilitating subsequent data classification and transmission, assisting in the rational allocation of data transmission resources, reducing latency and bandwidth occupancy during data transmission, improving the transmission efficiency and real-time performance of injury data, and supporting efficient rescue decision-making and scheduling.

[0084] As an optional embodiment, in the above steps, based on the clustering algorithm, multiple data sets are determined according to the location of the wounded and the injury sensing data, including:

[0085] Based on the location-related rules, all the injury sensing data are clustered according to the location of the wounded to obtain multiple location data sets; optionally, the location data set includes multiple injury sensing data with the distance between the corresponding wounded locations less than a preset first distance threshold;

[0086] For each location data set, based on the rules related to injury prediction, all the injury sensing data in the location data set are clustered to obtain at least one injury data set corresponding to the location data set;

[0087] According to at least one injury data set corresponding to each location data set, multiple data sets are determined based on the re-clustering rules.

[0088] It can be seen that through the above optional embodiments, the injury sensing data are clustered multiple times based on the location-related rules and the injury-related rules to obtain multiple data sets with close locations and similar injury conditions, improving the classification accuracy of injury data, facilitating the subsequent accurate determination of transmission devices, assisting in the rational allocation of data transmission resources, reducing latency and bandwidth occupancy during data transmission, improving the transmission efficiency and real-time performance of injury data, and supporting efficient rescue decision-making and scheduling.

[0089] As an optional embodiment, in the above steps, based on the rules related to injury prediction, all the injury sensing data in the location data set are clustered to obtain at least one injury data set corresponding to the location data set, including:

[0090] Each injury sensing data in the location data set is input into the trained injury prediction neural network to obtain the injury severity parameter corresponding to each injury sensing data; optionally, the injury prediction neural network is trained through a training data set including multiple training sensing data and corresponding injury annotations;

[0091] Cluster all the injury sensing data according to the injury severity parameter to obtain at least one injury data set corresponding to the location data set; the injury data set includes injury sensing data with a difference less than a preset first difference threshold between multiple corresponding injury severity parameters.

[0092] It can be seen that through the above optional embodiments, the injury severity parameter corresponding to each injury sensing data can be predicted according to the trained injury prediction neural network, and the data sets with similar injury conditions can be obtained by clustering based on the injury severity parameter, improving the classification accuracy of the injury data, so as to accurately determine the transmission device subsequently, assist in realizing the reasonable allocation of data transmission resources, reduce the delay and bandwidth occupation during the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0093] As an optional embodiment, in the above steps, based on at least one injury data set corresponding to each location data set, multiple data sets are determined according to the reclustering rule, including:

[0094] Calculate the average value of the positions of the wounded corresponding to all the injury sensing data in each injury data set to obtain the set position of each injury data set;

[0095] Calculate the average value of the injury severity parameters corresponding to all the injury sensing data in each injury data set to obtain the set injury parameter of each injury data set;

[0096] For injury data sets with a distance less than a second distance threshold between any two corresponding set positions, calculate the parameter difference between the set injury parameters corresponding to the two injury data sets;

[0097] Judge whether the parameter difference is less than the second difference threshold. If so, merge the two injury data sets into one data set; otherwise, keep them as two data sets respectively.

[0098] It can be seen that through the above optional embodiments, based on the calculation of the set position and the set injury, the data sets with close positions and similar injuries can be merged, thereby improving the clustering accuracy of the injury data, assisting in realizing the reasonable allocation of data transmission resources, reducing the delay and bandwidth occupation during the data transmission process, improving the transmission efficiency and real-time performance of the injury data, and supporting efficient rescue decision-making and scheduling.

[0099] As an optional embodiment, in the above steps, according to the data characteristics corresponding to each data set, the transmission device corresponding to each data set is determined, including:

[0100] For each data set, determine the portable devices of the wounded corresponding to all the injury sensing data in the data set to obtain multiple candidate devices;

[0101] According to the injury sensing data and the wounded location corresponding to each candidate device, determine the communication stability priority of each candidate device;

[0102] Determine the candidate device with the highest communication stability priority as the transmission device corresponding to the data set.

[0103] It can be seen that through the above optional embodiments, the communication stability priority of each candidate device can be determined based on the injury sensing data and the wounded location corresponding to the candidate device, so as to accurately screen out the most reasonable and efficient transmission device to transmit the injury sensing data, realize the reasonable allocation of data transmission resources, reduce the delay and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0104] As an optional embodiment, in the above steps, according to the injury sensing data and the wounded location corresponding to each candidate device, determining the communication stability priority of each candidate device includes:

[0105] Calculate the average value of the position distances between the wounded location corresponding to each candidate device and the wounded locations of all other candidate devices to obtain the position parameter corresponding to each candidate device;

[0106] Determine the injury severity parameter corresponding to the injury sensing data corresponding to each candidate device;

[0107] Calculate the ratio of the injury severity parameter and the position parameter corresponding to each candidate device to obtain the communication stability priority of each candidate device.

[0108] It can be seen that through the above optional embodiments, the communication stability priority of each candidate device can be determined based on the calculation of the ratio of the injury severity parameter and the wounded location, so that the device with a more serious injury and closer to other wounded devices is more likely to be selected as the transmission device, so as to realize the reasonable allocation of data transmission resources, reduce the delay and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0109] Embodiment 2

[0110] Please refer to Figure 2 , Figure 2 is a schematic structural diagram of a wounded sensing data transmission system based on clustering grouping disclosed in an embodiment of the present invention. Among them, Figure 2The described casualty sensing data transmission system based on clustering grouping can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 2 shown, the casualty sensing data transmission system based on clustering grouping may include:

[0111] An acquisition module 201, configured to acquire a plurality of injury sensing data to be transmitted of a plurality of casualties in an accident area.

[0112] A first determination module 202, configured to determine a plurality of data sets based on a clustering algorithm according to the positions of the casualties and the injury sensing data.

[0113] A second determination module 203, configured to determine a transmission device corresponding to each data set according to the data characteristics corresponding to each data set.

[0114] A transmission module 204, configured to send each data set to the corresponding transmission device so that the transmission device transmits the data set to a cloud device.

[0115] It can be seen that the above-mentioned invention embodiments can classify the data based on the injury sensing data to be transmitted of a plurality of casualties in the accident area, use a clustering algorithm to classify the data according to the positions of the casualties and the injury sensing data, determine the characteristics of each data set, and accordingly match a suitable transmission device, send the data set to the corresponding transmission device, and transmit it to the cloud device by the transmission device, so as to reasonably allocate data transmission resources, reduce the delay and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0116] As an optional embodiment, the injury sensing data includes at least one of physiological data, motion data, environmental sensing data, image data, and sound data; the physiological data includes at least one of heart rate, blood oxygen saturation, blood pressure, body temperature, respiratory rate, electrocardiogram data, blood glucose level, and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information, and limb mobility; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration, and light intensity.

[0117] It can be seen that through the above optional embodiment, the content of the injury sensing data is defined to comprehensively characterize the characteristics related to the injuries of the casualties, so as to facilitate subsequent data classification and transmission, assist in reasonably allocating data transmission resources, reducing the delay and bandwidth occupation in the data transmission process, improving the transmission efficiency and real-time performance of the injury data, and supporting efficient rescue decision-making and scheduling.

[0118] As an alternative embodiment, the injury condition sensing data is obtained by a sensor module in a portable device capable of data transmission on the casualty; the transmission device is the portable device.

[0119] It can be seen that through the above alternative embodiment, the acquisition method of the injury condition sensing data is defined, enabling the portable device to simultaneously acquire and transmit the injury condition data, facilitating subsequent data classification and transmission, assisting in the rational allocation of data transmission resources, reducing delays and bandwidth occupancy during data transmission, improving the transmission efficiency and real-time performance of the injury condition data, and supporting efficient rescue decision-making and dispatching.

[0120] As an alternative embodiment, the specific manner in which the first determination module determines multiple data sets based on the location of the casualty and the injury condition sensing data using a clustering algorithm includes:

[0121] Based on location-related rules, all the injury condition sensing data is clustered according to the location of the casualty to obtain multiple location data sets; optionally, the location data sets include injury condition sensing data where the distances between multiple corresponding casualty locations are less than a preset first distance threshold;

[0122] For each location data set, based on rules related to injury condition prediction, all the injury condition sensing data in the location data set is clustered to obtain at least one injury condition data set corresponding to the location data set;

[0123] Based on at least one injury condition data set corresponding to each location data set, multiple data sets are determined based on reclustering rules.

[0124] It can be seen that through the above alternative embodiment, the injury condition sensing data is clustered multiple times based on location-related rules and injury condition-related rules to obtain multiple data sets with close locations and similar injury conditions, improving the classification accuracy of the injury condition data, facilitating the subsequent accurate determination of the transmission device, assisting in the rational allocation of data transmission resources, reducing delays and bandwidth occupancy during data transmission, improving the transmission efficiency and real-time performance of the injury condition data, and supporting efficient rescue decision-making and dispatching.

[0125] As an alternative embodiment, the specific manner in which the first determination module clusters all the injury condition sensing data in the location data set based on rules related to injury condition prediction to obtain at least one injury condition data set corresponding to the location data set includes:

[0126] Each injury condition sensing data in the location data set is input into a trained injury condition prediction neural network to obtain an injury severity parameter corresponding to each injury condition sensing data; optionally, the injury condition prediction neural network is trained using a training data set including multiple training sensing data and corresponding injury condition annotations;

[0127] Cluster all the injury sensing data according to the injury severity parameter to obtain at least one injury data set corresponding to the location data set; the injury data set includes injury sensing data in which the difference between multiple corresponding injury severity parameters is less than a preset first difference threshold.

[0128] It can be seen that through the above optional embodiments, the injury severity parameter corresponding to each injury sensing data can be predicted according to the trained injury prediction neural network, and the data sets with similar injury conditions can be obtained by clustering based on the injury severity parameter, improving the classification accuracy of the injury data, so as to accurately determine the transmission device subsequently, assist in realizing the reasonable allocation of data transmission resources, reduce the delay and bandwidth occupation during the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0129] As an optional embodiment, the specific manner in which the first determination module determines multiple data sets based on at least one injury data set corresponding to each location data set and based on the reclustering rule includes:

[0130] Calculate the average value of the positions of the wounded corresponding to all the injury sensing data in each injury data set to obtain the set position of each injury data set;

[0131] Calculate the average value of the injury severity parameters corresponding to all the injury sensing data in each injury data set to obtain the set injury parameter of each injury data set;

[0132] For injury data sets in which the distance between any two corresponding set positions is less than a second distance threshold, calculate the parameter difference between the set injury parameters corresponding to the two injury data sets;

[0133] Judge whether the parameter difference is less than a second difference threshold. If so, merge the two injury data sets into one data set; otherwise, keep them as two data sets respectively.

[0134] It can be seen that through the above optional embodiments, based on the calculation of the set position and the set injury, the data sets with close positions and similar injuries can be merged, thereby improving the clustering accuracy of the injury data, assisting in realizing the reasonable allocation of data transmission resources, reducing the delay and bandwidth occupation during the data transmission process, improving the transmission efficiency and real-time performance of the injury data, and supporting efficient rescue decision-making and scheduling.

[0135] As an optional embodiment, the specific manner in which the second determination module determines the transmission device corresponding to each data set according to the data characteristics corresponding to each data set includes:

[0136] For each data set, determine the portable devices of the wounded corresponding to all the injury sensing data in the data set to obtain multiple candidate devices;

[0137] According to the injury sensing data and the wounded location corresponding to each candidate device, determine the communication stability priority of each candidate device;

[0138] Determine the candidate device with the highest communication stability priority as the transmission device corresponding to the data set.

[0139] It can be seen that through the above optional embodiments, the communication stability priority of each candidate device can be determined based on the injury sensing data and the wounded location corresponding to the candidate device, so as to accurately screen out the most reasonable and efficient transmission device to transmit the injury sensing data, realize the reasonable allocation of data transmission resources, reduce the delay and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0140] As an optional embodiment, the specific manner in which the second determination module determines the communication stability priority of each candidate device according to the injury sensing data and the wounded location corresponding to each candidate device includes:

[0141] Calculate the average value of the position distances between the wounded location corresponding to each candidate device and the wounded locations of all other candidate devices to obtain the position parameter corresponding to each candidate device;

[0142] Determine the injury severity parameter corresponding to the injury sensing data corresponding to each candidate device;

[0143] Calculate the ratio of the injury severity parameter and the position parameter corresponding to each candidate device to obtain the communication stability priority of each candidate device.

[0144] It can be seen that through the above optional embodiments, the communication stability priority of each candidate device can be determined based on the calculation of the ratio of the injury severity parameter and the wounded location, so that the device with a more serious injury and closer to other wounded devices is more likely to be selected as the transmission device, so as to realize the reasonable allocation of data transmission resources, reduce the delay and bandwidth occupation in the data transmission process, improve the transmission efficiency and real-time performance of the injury data, and support efficient rescue decision-making and scheduling.

[0145] Embodiment III

[0146] Please refer to Figure 3 , Figure 3 which is another wounded sensing data transmission system based on clustering grouping disclosed in the embodiments of the present invention. Figure 3The described casualty sensing data transmission system based on clustering and grouping is applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 3 shown, the casualty sensing data transmission system based on clustering and grouping may include:

[0147] A memory 301 storing executable program code;

[0148] A processor 302 coupled to the memory 301;

[0149] Wherein, the processor 302 invokes the executable program code stored in the memory 301 to execute the steps of the casualty sensing data transmission method described in Embodiment 1.

[0150] Embodiment 4

[0151] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the casualty sensing data transmission method described in Embodiment 1.

[0152] Embodiment 5

[0153] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the casualty sensing data transmission method described in Embodiment 1.

[0154] The above describes specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily have to be performed in the particular order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0155] The systems, devices, modules, or units illustrated in the above embodiments may be specifically implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0156] For the convenience of description, when describing the above device, it is divided into various units according to functions for separate description. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0157] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0158] This specification is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0159] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0161] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0162] Memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0163] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0164] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element qualified by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0165] This specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0166] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiment.

[0167] Finally, it should be noted that the method and system for transmitting wounded personnel sensing data based on clustering and grouping disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention. It is only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for transmitting casualty sensing data based on clustering and grouping, characterized in that, The method includes: Obtaining a plurality of injury sensing data to be transmitted for a plurality of injured persons within the accident area; Based on the positions of the injured persons and the injury sensing data, and based on a clustering algorithm, determining a plurality of data sets, including: Based on position-related rules, clustering all the injury sensing data according to the positions of the injured persons to obtain a plurality of position data sets; the position data sets include the injury sensing data for which the distances between a plurality of corresponding positions of the injured persons are less than a preset first distance threshold; For each of the position data sets, based on rules related to injury prediction, clustering all the injury sensing data in the position data set to obtain at least one injury data set corresponding to the position data set; Based on the at least one injury data set corresponding to each position data set, and based on reclustering rules, determining a plurality of data sets; According to the data characteristics corresponding to each data set, determining a transmission device corresponding to each data set; Sending each data set to the corresponding transmission device for the transmission device to transmit the data set to a cloud device.

2. The method for transmitting wounded personnel sensing data based on clustering grouping according to claim 1, wherein, The injury sensing data includes at least one of physiological data, motion data, environmental sensing data, image data, and sound data; the physiological data includes at least one of heart rate, blood oxygen saturation, blood pressure, body temperature, respiratory rate, electrocardiogram data, blood glucose level, and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information, and limb mobility; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration, and light intensity.

3. The method for transmitting wounded personnel sensing data based on clustering and grouping according to claim 1, characterized in that The injury sensing data is obtained through a sensor module in a portable device capable of data transmission on the injured person; The transmission device is the portable device.

4. The method for transmitting casualty sensing data based on clustering grouping according to claim 1, wherein The clustering of all the injury sensing data in the position data set based on rules related to injury prediction to obtain at least one injury data set corresponding to the position data set includes: Inputting each injury sensing data in the position data set into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each injury sensing data; the injury prediction neural network is trained through a training data set including a plurality of training sensing data and corresponding injury annotations; Based on the injury severity parameters, clustering all the injury sensing data to obtain at least one injury data set corresponding to the position data set; the injury data set includes the injury sensing data for which the differences between a plurality of corresponding injury severity parameters are less than a preset first difference threshold.

5. The method for transmitting casualty sensing data based on clustering grouping according to claim 4, wherein The determining of a plurality of data sets based on the at least one injury data set corresponding to each position data set and based on reclustering rules includes: Calculating the average value of the positions of the injured persons corresponding to all the injury sensing data in each injury data set to obtain the set position of each injury data set; Calculate the average value of the injury severity parameters corresponding to all the injury sensing data in each of the injury data sets to obtain the set injury parameter of each of the injury data sets; For the injury data sets where the distance between any two corresponding set positions is less than a second distance threshold, calculate the parameter difference between the set injury parameters corresponding to the two injury data sets; Determine whether the parameter difference is less than a second difference threshold. If so, merge the two injury data sets into one data set; otherwise, keep them as two separate data sets.

6. The method for transmitting casualty sensing data based on clustering grouping according to claim 4, wherein The determining of the transmission device corresponding to each data set according to the data characteristics corresponding to each data set includes: For each data set, determine the portable devices of the injured corresponding to all the injury sensing data in the data set to obtain a plurality of candidate devices; Determine the communication stability priority of each candidate device according to the injury sensing data and the location of the injured corresponding to each candidate device; Determine the candidate device with the highest communication stability priority as the transmission device corresponding to the data set.

7. The method for transmitting casualty sensing data based on clustering grouping according to claim 6, characterized in that, The determining of the communication stability priority of each candidate device according to the injury sensing data and the location of the injured corresponding to each candidate device includes: Calculate the average value of the position distances between the location of the injured corresponding to each candidate device and the locations of all other candidate devices to obtain the position parameter corresponding to each candidate device; Determine the injury severity parameter corresponding to the injury sensing data corresponding to each candidate device; Calculate the ratio of the injury severity parameter and the position parameter corresponding to each candidate device to obtain the communication stability priority of each candidate device.

8. An injured person sensing data transmission system based on clustering and grouping, characterized in that, The system includes: An acquisition module, configured to acquire a plurality of injury sensing data to be transmitted of a plurality of injured in an accident area; A first determination module, configured to determine a plurality of data sets based on a clustering algorithm according to the location of the injured and the injury sensing data, including: Based on a location-related rule, cluster all the injury sensing data according to the location of the injured to obtain a plurality of location data sets; the location data sets include injury sensing data where the distance between a plurality of corresponding injured locations is less than a preset first distance threshold; For each location data set, based on a rule related to injury prediction, cluster all the injury sensing data in the location data set to obtain at least one injury data set corresponding to the location data set; Based on the at least one injury data set corresponding to each location data set, determine a plurality of data sets based on a re-clustering rule; A second determination module, configured to determine the transmission device corresponding to each data set according to the data characteristics corresponding to each data set; A transmission module, configured to send each data set to the corresponding transmission device for the transmission device to transmit the data set to a cloud device.

9. A wounded personnel sensing data transmission system based on clustering and grouping, characterized in that The system includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory and executes the method for transmitting casualty sensing data based on clustering grouping according to any one of claims 1-7.

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