Clustering grouping-based wounded sensing data transmission method and system

Abstract

The invention discloses a cluster grouping-based wounded person sensing data transmission method and system. The method comprises the following steps of: obtaining a plurality of to-be-transmitted injury condition sensing data of a plurality of wounded persons in an accident area; determining a plurality of data sets based on a clustering algorithm according to the position of the wounded and the injury condition sensing data; determining transmission equipment 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 so that the transmission device transmits the data set to a cloud device. Therefore, the method can reasonably allocate data transmission resources, reduce delay and bandwidth occupation in the data transmission process, and improve the transmission efficiency and real-time performance of the injury condition data so as to support efficient rescue decision and scheduling.

Description

Technical Field

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

[0002] In existing accident emergency rescue, it is usually necessary to obtain the injury information of the injured in real time and transmit the relevant data to the cloud device to assist in rescue decision-making. In the existing technology, fixed transmission equipment or a single transmission path is usually used for data transmission. However, in the case of complex accident environments and uneven distribution of the injured, the fixed transmission method may lead to inefficient transmission of some data, and even data delays or loss. In addition, the existing technology lacks grouping strategies for different data characteristics and cannot optimize the transmission path according to data characteristics, resulting in unbalanced data transmission load, affecting the overall rescue efficiency and the timeliness of decision-making. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

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

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for transmitting sensor data of a wounded person based on clustering grouping, the method comprising: Acquire multiple injury sensor data to be transmitted of multiple injured persons in the accident area; Determining multiple data sets based on the location of the injured person and the injury sensor data and based on a clustering algorithm; Determining the transmission device corresponding to each of the data sets according to the data characteristics corresponding to each of the data sets; Each of the data sets is sent to the corresponding transmission device so that the transmission device transmits the data set to a cloud device.

[0005] As an optional embodiment, 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 sugar level and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information and limb activity; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration and light intensity.

[0006] As an optional implementation, 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 and carried by the injured person; the transmission device is the portable device.

[0007] As an optional implementation, in the first aspect of the present invention, the multiple data sets are determined based on the location of the injured person and the injury sensor data based on a clustering algorithm, including: Based on the location-related rules, all the injury sensing data are clustered according to the location of the injured person to obtain a plurality of location data sets; the location data sets include the injury sensing data whose distances between the locations of the multiple injured persons are less than a preset first distance threshold; For each of the location data sets, clustering all the injury condition sensor 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; According to at least one injury data set corresponding to each of the location data sets, multiple data sets are determined based on re-clustering rules.

[0008] As an optional implementation, in the first aspect of the present invention, clustering all the injury sensor data in the location data set based on the rules related to injury prediction to obtain at least one injury data set corresponding to the location data set includes: Inputting each of the injury sensor data in the position data set into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each of the injury sensor data; the injury prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding injury annotations; According to the injury severity parameter, all the injury sensor data are clustered to obtain at least one injury data set corresponding to the location data set; the injury data set includes the injury sensor data whose differences between multiple corresponding injury severity parameters are less than a preset first difference threshold.

[0009] As an optional implementation, in the first aspect of the present invention, the at least one injury data set corresponding to each of the location data sets is determined based on a re-clustering rule to obtain multiple data sets, including: Calculating the average value of the injured person's positions corresponding to all the injury condition sensor data in each injury condition data set to obtain the set position of each injury condition data set; Calculating the average value of the injury severity parameters corresponding to all the injury sensor data in each injury data set to obtain a set injury parameter of each injury data set; For any two injury data sets whose distance between corresponding set positions is less than a second distance threshold, calculating a parameter difference between 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, retain them as two data sets.

[0010] As an optional implementation manner, in the first aspect of the present invention, determining the transmission device corresponding to each of the data sets according to the data characteristics corresponding to each of the data sets includes: For each of the data sets, determining the portable devices of the injured person corresponding to all the injury sensing data in the data set, and obtaining a plurality of candidate devices; Determine the communication stability priority of each candidate device according to the injury sensing data and the injured person's location corresponding to each candidate device; The candidate device with the highest communication stability priority is determined as the transmission device corresponding to the data set.

[0011] As an optional implementation, 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 injured person location corresponding to each candidate device includes: Calculate the average of the position distances between the injured person position corresponding to each candidate device and the injured person positions 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 of the candidate devices; The ratio of the injury severity parameter and the location parameter corresponding to each of the candidate devices is calculated to obtain the communication stability priority of each of the candidate devices.

[0012] A second aspect of an embodiment of the present invention discloses a system for transmitting sensor data of a wounded person based on clustering grouping, the system comprising: An acquisition module, used for acquiring a plurality of injury sensor data to be transmitted of a plurality of injured persons in the accident area; A first determination module is used to determine multiple data sets based on a clustering algorithm according to the position of the injured person and the injury sensor data; A second determination module, configured to determine a transmission device corresponding to each of the data sets according to data characteristics corresponding to each of the data sets; The transmission module is used to send each of the data sets to the corresponding transmission device so that the transmission device transmits the data set to the cloud device.

[0013] As an optional embodiment, 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 sugar level and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information and limb activity; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration and light intensity.

[0014] As an optional implementation, 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 and carried by the injured person; the transmission device is the portable device.

[0015] As an optional implementation, in the second aspect of the present invention, the first determination module determines the specific manner of the multiple data sets based on the location of the injured person and the injury sensor data based on a clustering algorithm, including: Based on the location-related rules, all the injury sensing data are clustered according to the location of the injured person to obtain a plurality of location data sets; the location data sets include the injury sensing data whose distances between the locations of the multiple injured persons are less than a preset first distance threshold; For each of the location data sets, clustering all the injury condition sensor 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; According to at least one injury data set corresponding to each of the location data sets, multiple data sets are determined based on re-clustering rules.

[0016] As an optional implementation, in the second aspect of the present invention, the first determination module clusters all the injury sensor data in the location data set based on rules related to injury prediction to obtain a specific manner of at least one injury data set corresponding to the location data set, including: Inputting each of the injury sensor data in the position data set into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each of the injury sensor data; the injury prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding injury annotations; According to the injury severity parameter, all the injury sensor data are clustered to obtain at least one injury data set corresponding to the location data set; the injury data set includes the injury sensor data whose differences between multiple corresponding injury severity parameters are less than a preset first difference threshold.

[0017] As an optional implementation, in the second aspect of the present invention, the first determination module determines a specific manner of multiple data sets based on the re-clustering rule according to at least one injury data set corresponding to each of the location data sets, including: Calculating the average value of the injured person's positions corresponding to all the injury condition sensor data in each injury condition data set to obtain the set position of each injury condition data set; Calculating the average value of the injury severity parameters corresponding to all the injury sensor data in each injury data set to obtain a set injury parameter of each injury data set; For any two injury data sets whose distance between corresponding set positions is less than a second distance threshold, calculating a parameter difference between 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, retain them as two data sets.

[0018] As an optional implementation, in the second aspect of the present invention, the second determination module determines the specific mode of the transmission device corresponding to each of the data sets according to the data characteristics corresponding to each of the data sets, including: For each of the data sets, determining the portable devices of the injured person corresponding to all the injury sensing data in the data set, and obtaining a plurality of candidate devices; Determine the communication stability priority of each candidate device according to the injury sensing data and the injured person's location corresponding to each candidate device; The candidate device with the highest communication stability priority is determined as the transmission device corresponding to the data set.

[0019] As an optional implementation, in the second aspect of the present invention, the second determination module determines the specific manner of the communication stability priority of each candidate device according to the injury sensing data and the injured person location corresponding to each candidate device, including: Calculate the average of the position distances between the injured person position corresponding to each candidate device and the injured person positions 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 of the candidate devices; The ratio of the injury severity parameter and the location parameter corresponding to each of the candidate devices is calculated to obtain the communication stability priority of each of the candidate devices.

[0020] The third aspect of the present invention discloses another wounded sensor data transmission system based on clustering grouping, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the clustering-based grouping method for transmitting sensor data of a wounded person disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the clustering grouping based wounded sensor data transmission method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can classify the injury sensor data to be transmitted of multiple injured persons in the accident area according to the injured persons' positions and injury sensor data using a clustering algorithm, determine the characteristics of each data set, and match the appropriate transmission equipment accordingly, send the data set to the corresponding transmission equipment, and transmit it to the cloud device, thereby reasonably allocating data transmission resources, reducing delays and bandwidth occupancy during data transmission, and improving the transmission efficiency and real-time performance of injury data to support efficient rescue decision-making and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 The present invention is a flowchart of a method for transmitting sensor data of a wounded person based on clustering grouping disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a wounded sensor data transmission system based on clustering grouping disclosed in an embodiment of the present invention.

[0026] Figure 3It is a structural schematic diagram of another wounded sensor data transmission system based on clustering grouping disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification 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 "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a method and system for transmitting sensor data of injured persons based on clustering grouping, which can classify the injury sensor data to be transmitted of multiple injured persons in the accident area according to the injured person's position and injury sensor data using a clustering algorithm, determine the characteristics of each data set, and match the appropriate transmission equipment accordingly, send the data set to the corresponding transmission equipment, and transmit it to the cloud 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 injury data, and support efficient rescue decision-making and scheduling. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 1 is a flow chart of a method for transmitting sensor data of a wounded person based on clustering grouping disclosed in an embodiment of the present invention. Figure 1The described clustering-based wounded sensor data transmission method 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). Figure 1 As shown, the clustering-based wounded sensor data transmission method may include the following operations: 101. Obtain multiple injury sensor data to be transmitted of multiple injured persons in the accident area.

[0032] 102. According to the location of the injured and the injury sensor data, multiple data sets are determined based on the clustering algorithm. 103. Determine the transmission device corresponding to each data set according to the data characteristics corresponding to each data set. 104. Send each data set to a corresponding transmission device so that the transmission device transmits the data set to a cloud device.

[0033] It can be seen that the above-mentioned embodiments of the invention can classify the injury sensor data to be transmitted of multiple injured persons in the accident area according to the injured person's location and injury sensor data using a clustering algorithm, determine the characteristics of each data set, and match the appropriate transmission equipment accordingly, send the data set to the corresponding transmission equipment, and transmit it to the cloud device, so as to reasonably allocate data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of injury data to support efficient rescue decision-making and scheduling.

[0034] 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 sugar level and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information and limb activity; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration and light intensity.

[0035] It can be seen that through the above optional embodiments, the content of the injury sensor data is limited to comprehensively characterize the characteristics related to the injuries of the injured, so as to facilitate subsequent data classification and transmission, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy during data transmission, and improve the transmission efficiency and real-time performance of injury data to support efficient rescue decision-making and scheduling.

[0036] 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 and carried by the injured person; the transmission device is a portable device.

[0037] It can be seen that through the above optional embodiments, the method of acquiring injury sensor data is limited, so that the portable device can simultaneously acquire and transmit the injury data, so as to facilitate subsequent data classification and transmission, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data, so as to support efficient rescue decision-making and scheduling.

[0038] As an optional embodiment, in the above steps, according to the position of the injured person and the injury sensor data, based on a clustering algorithm, multiple data sets are determined, including: Based on the location-related rules, all the injury sensor data are clustered according to the location of the injured person to obtain multiple location data sets; optionally, the location data sets include multiple corresponding injury sensor data whose distances between the injured person's locations are less than a preset first distance threshold; For each location data set, clustering all injury condition sensor 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; According to at least one injury data set corresponding to each location data set, multiple data sets are determined based on re-clustering rules.

[0039] It can be seen that through the above-mentioned optional embodiments, the injury sensor data is clustered multiple times based on location-related rules and injury-related rules to obtain multiple data sets with similar locations and injury conditions, thereby improving the classification accuracy of the injury data, so as to facilitate the subsequent accurate determination of the transmission equipment, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data to support efficient rescue decision-making and scheduling.

[0040] As an optional embodiment, in the above steps, based on the rules related to injury prediction, all the injury sensor data in the location data set are clustered to obtain at least one injury data set corresponding to the location data set, including: Input each injury sensor data in the location data set into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each injury sensor data; optionally, the injury prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding injury annotations; All injury sensor data are clustered 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 sensor data whose differences between multiple corresponding injury severity parameters are less than a preset first difference threshold.

[0041] It can be seen that through the above-mentioned optional embodiments, the injury severity parameters corresponding to each injury sensor data can be predicted according to the trained injury prediction neural network, and clustering can be performed based on the injury severity parameters to obtain a data set with similar injury conditions, thereby improving the classification accuracy of the injury data, so as to facilitate the subsequent accurate determination of the transmission equipment, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data to support efficient rescue decision-making and scheduling.

[0042] As an optional embodiment, in the above steps, according to at least one injury condition data set corresponding to each location data set, multiple data sets are determined based on a re-clustering rule, including: Calculate the average value of the injured person's position corresponding to all the injury sensor 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 sensor data in each injury data set to obtain the set injury parameter of each injury data set; For any two injury data sets whose distance between corresponding set positions is less than a second distance threshold, calculating a parameter difference between set injury parameters corresponding to the two injury data sets; It is determined whether the parameter difference is less than a second difference threshold value. If so, the two injury data sets are merged into one data set; otherwise, they are retained as two data sets.

[0043] It can be seen that through the above-mentioned optional embodiments, it is possible to merge data sets with similar locations and injuries based on the calculation of the set location and the set injury, thereby improving the clustering accuracy of the injury data, assisting in the reasonable allocation of data transmission resources, reducing delays and bandwidth occupancy during data transmission, and improving the transmission efficiency and real-time performance of the injury data to support efficient rescue decision-making and scheduling.

[0044] As an optional embodiment, in the above step, determining 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 person corresponding to all the injury sensor data in the data set to obtain multiple candidate devices; Determine the communication stability priority of each candidate device according to the injury sensor data and the injured person's location corresponding to each candidate device; The candidate device with the highest priority for communication stability is determined as the transmission device corresponding to the data set.

[0045] It can be seen that through the above-mentioned optional embodiments, the communication stability priority of each candidate device can be determined based on the injury sensor data and the position of the injured person corresponding to the candidate device, so as to accurately screen out the most reasonable and efficient transmission device to transmit the injury sensor data, realize reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data, so as to support efficient rescue decision-making and scheduling.

[0046] As an optional embodiment, in the above steps, determining the communication stability priority of each candidate device according to the injury sensor data and the injured person's location corresponding to each candidate device includes: Calculate the average of the position distances between the injured person position corresponding to each candidate device and the injured person positions of all other candidate devices to obtain the position parameter corresponding to each candidate device; Determine the injury severity parameter corresponding to the injury sensor data corresponding to each candidate device; The ratio of the injury severity parameter and the location parameter corresponding to each candidate device is calculated to obtain the communication stability priority of each candidate device.

[0047] It can be seen that through the above-mentioned optional embodiments, the communication stability priority of each candidate device can be determined based on the ratio calculation of the injury severity parameter and the injured person's location, so that devices with more serious injuries and closer to other injured devices are more likely to be selected as transmission devices, so as to achieve reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy during data transmission, and improve the transmission efficiency and real-time performance of injury data, so as to support efficient rescue decision-making and scheduling.

[0048] Embodiment 2 See also Figure 2 , Figure 2 1 is a schematic diagram of a structure of a wounded sensor data transmission system based on clustering grouping disclosed in an embodiment of the present invention. Figure 2 The described wounded sensor 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). Figure 2 As shown, the wounded sensor data transmission system based on clustering grouping may include: The acquisition module 201 is used to acquire a plurality of injury sensor data to be transmitted of a plurality of injured persons in the accident area.

[0049] The first determination module 202 is used to determine multiple data sets based on the location of the injured person and the injury sensor data based on a clustering algorithm. The second determination module 203 is used to determine the transmission device corresponding to each data set according to the data characteristics corresponding to each data set. The transmission module 204 is used to send each data set to a corresponding transmission device so that the transmission device transmits the data set to the cloud device.

[0050] It can be seen that the above-mentioned embodiments of the invention can classify the injury sensor data to be transmitted of multiple injured persons in the accident area according to the injured person's location and injury sensor data using a clustering algorithm, determine the characteristics of each data set, and match the appropriate transmission equipment accordingly, send the data set to the corresponding transmission equipment, and transmit it to the cloud device, so as to reasonably allocate data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of injury data to support efficient rescue decision-making and scheduling.

[0051] 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 sugar level and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information and limb activity; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration and light intensity.

[0052] It can be seen that through the above optional embodiments, the content of the injury sensor data is limited to comprehensively characterize the characteristics related to the injuries of the injured, so as to facilitate subsequent data classification and transmission, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy during data transmission, and improve the transmission efficiency and real-time performance of injury data to support efficient rescue decision-making and scheduling.

[0053] As an optional embodiment, the injury sensing data is obtained through a sensor module in a portable device capable of data transmission and carried by the injured person; the transmission device is a portable device.

[0054] It can be seen that through the above optional embodiments, the method of acquiring injury sensor data is limited, so that the portable device can simultaneously acquire and transmit the injury data, so as to facilitate subsequent data classification and transmission, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data, so as to support efficient rescue decision-making and scheduling.

[0055] As an optional embodiment, the first determination module determines the specific manner of the multiple data sets according to the location of the injured person and the injury sensor data based on the clustering algorithm, including: Based on the location-related rules, all the injury sensor data are clustered according to the location of the injured person to obtain multiple location data sets; optionally, the location data sets include multiple corresponding injury sensor data whose distances between the injured person's locations are less than a preset first distance threshold; For each location data set, clustering all injury condition sensor 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; According to at least one injury data set corresponding to each location data set, multiple data sets are determined based on re-clustering rules.

[0056] It can be seen that through the above-mentioned optional embodiments, the injury sensor data is clustered multiple times based on location-related rules and injury-related rules to obtain multiple data sets with similar locations and injury conditions, thereby improving the classification accuracy of the injury data, so as to facilitate the subsequent accurate determination of the transmission equipment, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data to support efficient rescue decision-making and scheduling.

[0057] As an optional embodiment, the first determination module clusters all injury sensor data in the location data set based on rules related to injury prediction to obtain a specific manner of at least one injury data set corresponding to the location data set, including: Input each injury sensor data in the location data set into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each injury sensor data; optionally, the injury prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding injury annotations; All injury sensor data are clustered 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 sensor data whose differences between multiple corresponding injury severity parameters are less than a preset first difference threshold.

[0058] It can be seen that through the above-mentioned optional embodiments, the injury severity parameters corresponding to each injury sensor data can be predicted according to the trained injury prediction neural network, and clustering can be performed based on the injury severity parameters to obtain a data set with similar injury conditions, thereby improving the classification accuracy of the injury data, so as to facilitate the subsequent accurate determination of the transmission equipment, assist in the reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data to support efficient rescue decision-making and scheduling.

[0059] As an optional embodiment, the first determination module determines a specific manner of multiple data sets based on the re-clustering rule according to at least one injury condition data set corresponding to each location data set, including: Calculate the average value of the injured person's position corresponding to all the injury sensor 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 sensor data in each injury data set to obtain the set injury parameter of each injury data set; For any two injury data sets whose distance between corresponding set positions is less than a second distance threshold, calculating a parameter difference between set injury parameters corresponding to the two injury data sets; It is determined whether the parameter difference is less than a second difference threshold value. If so, the two injury data sets are merged into one data set; otherwise, they are retained as two data sets.

[0060] It can be seen that through the above-mentioned optional embodiments, it is possible to merge data sets with similar locations and injuries based on the calculation of the set location and the set injury, thereby improving the clustering accuracy of the injury data, assisting in the reasonable allocation of data transmission resources, reducing delays and bandwidth occupancy during data transmission, and improving the transmission efficiency and real-time performance of the injury data to support efficient rescue decision-making and scheduling.

[0061] As an optional embodiment, the second determining module determines the specific mode of the transmission device corresponding to each data set according to the data characteristics corresponding to each data set, including: For each data set, determine the portable devices of the injured person corresponding to all the injury sensor data in the data set to obtain multiple candidate devices; Determine the communication stability priority of each candidate device according to the injury sensor data and the injured person's location corresponding to each candidate device; The candidate device with the highest priority for communication stability is determined as the transmission device corresponding to the data set.

[0062] It can be seen that through the above-mentioned optional embodiments, the communication stability priority of each candidate device can be determined based on the injury sensor data and the position of the injured person corresponding to the candidate device, so as to accurately screen out the most reasonable and efficient transmission device to transmit the injury sensor data, realize reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy in the data transmission process, and improve the transmission efficiency and real-time performance of the injury data, so as to support efficient rescue decision-making and scheduling.

[0063] As an optional embodiment, the second determination module determines the specific manner of the communication stability priority of each candidate device according to the injury sensor data and the injured person location corresponding to each candidate device, including: Calculate the average of the position distances between the injured person position corresponding to each candidate device and the injured person positions of all other candidate devices to obtain the position parameter corresponding to each candidate device; Determine the injury severity parameter corresponding to the injury sensor data corresponding to each candidate device; The ratio of the injury severity parameter and the location parameter corresponding to each candidate device is calculated to obtain the communication stability priority of each candidate device.

[0064] It can be seen that through the above-mentioned optional embodiments, the communication stability priority of each candidate device can be determined based on the ratio calculation of the injury severity parameter and the injured person's location, so that devices with more serious injuries and closer to other injured devices are more likely to be selected as transmission devices, so as to achieve reasonable allocation of data transmission resources, reduce delays and bandwidth occupancy during data transmission, and improve the transmission efficiency and real-time performance of injury data, so as to support efficient rescue decision-making and scheduling.

[0065] Embodiment 3 See also Figure 3 , Figure 3 This is another wounded person sensor data transmission system based on clustering grouping disclosed in an embodiment of the present invention. Figure 3 The described clustering-based wounded sensor data transmission system 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). Figure 3 As shown, the wounded sensor data transmission system based on clustering grouping may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the clustering-based wounded sensor data transmission method described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for transmitting sensor data of a wounded person based on clustering grouping as described in the first embodiment.

[0067] Embodiment 5 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 enable a computer to execute the steps of the cluster grouping-based wounded sensor data transmission method described in Example 1.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products 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 a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in 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.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may 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 specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and 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, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the clustering-based wounded sensor data transmission method and system disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for transmitting sensor data of injured persons based on clustering grouping, characterized in that: The method comprises: Acquire multiple injury sensor data to be transmitted of multiple injured persons in the accident area; Determining multiple data sets based on the location of the injured person and the injury sensor data and based on a clustering algorithm; Determining the transmission device corresponding to each of the data sets according to the data characteristics corresponding to each of the data sets; Each of the data sets is sent to the corresponding transmission device so that the transmission device transmits the data set to a cloud device.

2. The method for transmitting sensor data of a wounded person based on clustering grouping according to claim 1, characterized in that: 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 sugar level and bleeding detection data; the motion data includes at least one of acceleration, angular velocity, displacement information and limb activity; the environmental sensing data includes at least one of temperature, humidity, air quality, dust concentration and light intensity.

3. The method for transmitting sensor data of a wounded person based on clustering 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 sensor data of a wounded person based on clustering grouping according to claim 1, characterized in that: The method further comprises determining a plurality of data sets based on the location of the injured person and the injury sensor data and a clustering algorithm, including: Based on the location-related rules, all the injury sensing data are clustered according to the location of the injured person to obtain a plurality of location data sets; the location data sets include the injury sensing data whose distances between the locations of the multiple injured persons are less than a preset first distance threshold; For each of the location data sets, clustering all the injury condition sensor 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; According to at least one injury data set corresponding to each of the location data sets, multiple data sets are determined based on re-clustering rules.

5. The method for transmitting wounded sensor data based on clustering grouping according to claim 4 is characterized in that: The clustering of all the injury condition sensor data in the location data set based on the rules related to injury condition prediction to obtain at least one injury condition data set corresponding to the location data set includes: Inputting each of the injury sensor data in the position data set into a trained injury prediction neural network to obtain an injury severity parameter corresponding to each of the injury sensor data; the injury prediction neural network is trained by a training data set including a plurality of training sensor data and corresponding injury annotations; According to the injury severity parameter, all the injury sensor data are clustered to obtain at least one injury data set corresponding to the location data set; the injury data set includes the injury sensor data whose differences between multiple corresponding injury severity parameters are less than a preset first difference threshold.

6. The method for transmitting sensor data of a wounded person based on clustering grouping according to claim 5, characterized in that: The determining of multiple data sets based on the re-clustering rule according to at least one injury data set corresponding to each of the location data sets includes: Calculating the average value of the injured person's positions corresponding to all the injury condition sensor data in each injury condition data set to obtain the set position of each injury condition data set; Calculating the average value of the injury severity parameters corresponding to all the injury sensor data in each injury data set to obtain a set injury parameter of each injury data set; For any two injury data sets whose distance between corresponding set positions is less than a second distance threshold, calculating a parameter difference between 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, retain them as two data sets.

7. The method for transmitting sensor data of a wounded person based on clustering grouping according to claim 5, characterized in that: The step of determining the transmission device corresponding to each data set according to the data characteristics corresponding to each data set includes: For each of the data sets, determining the portable devices of the injured person corresponding to all the injury sensing data in the data set, and obtaining a plurality of candidate devices; Determine the communication stability priority of each candidate device according to the injury sensing data and the injured person's location corresponding to each candidate device; The candidate device with the highest communication stability priority is determined as the transmission device corresponding to the data set.

8. The method for transmitting sensor data of a wounded person based on clustering grouping according to claim 7, characterized in that: Determining the communication stability priority of each candidate device according to the injury sensing data and the injured person's position corresponding to each candidate device includes: Calculate the average of the position distances between the injured person position corresponding to each candidate device and the injured person positions 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 of the candidate devices; The ratio of the injury severity parameter and the location parameter corresponding to each of the candidate devices is calculated to obtain the communication stability priority of each of the candidate devices.

9. A wounded sensor data transmission system based on clustering grouping, characterized in that: The system comprises: An acquisition module, used for acquiring a plurality of injury sensor data to be transmitted of a plurality of injured persons in the accident area; A first determination module is used to determine multiple data sets based on a clustering algorithm according to the position of the injured person and the injury sensor data; A second determination module, configured to determine a transmission device corresponding to each of the data sets according to data characteristics corresponding to each of the data sets; The transmission module is used to send each of the data sets to the corresponding transmission device so that the transmission device transmits the data set to the cloud device.

10. A wounded sensor data transmission system based on clustering grouping, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the clustering grouping-based wounded sensor data transmission method according to any one of claims 1 to 8.

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