Accidental injury monitoring method, system and equipment and computer readable storage medium

Abstract

The invention discloses an accidental injury monitoring method, system and device and a computer readable storage medium, and the method comprises the steps: collecting the biological characteristic data of a user and the current environment data; calling a preset baseline model and / or an accident scene model, and taking data meeting a preset condition in the biological characteristic data and / or the environment data as associated information; analyzing the associated information, and determining an accidental injury scene and an accidental injury level of the user; and generating an alarm event corresponding to the accidental injury scene, and providing a processing suggestion corresponding to the accidental injury level. According to the invention, an accidental injury monitoring scheme with higher universality and higher accuracy is realized, the monitoring scene is enriched, the application universality and the function expansibility are improved, and the user experience is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of safety monitoring technology, and in particular to an accidental injury monitoring method, system, device and computer-readable storage medium. Background Art

[0002] In people's daily lives, accidental injuries occur frequently, and it is particularly important to detect and deal with these incidents in a timely manner.

[0003] At present, there are accidental injury detection solutions for some scenarios, such as fall detection, car accident detection, etc.; however, the judgment basis of these detection solutions is only a small number of preset feature parameters, which are only applicable to single detection terminals and fixed application scenarios.

[0004] In summary, the existing accidental injury monitoring solutions have the defects of single monitoring scenario, large applicability limitations and poor functional scalability. Summary of the invention

[0005] In order to overcome the deficiencies in the prior art, the purpose of the present invention is to provide an accidental injury monitoring method, system, device and computer-readable storage medium to solve the problems existing in the current accidental injury monitoring scheme, such as single monitoring scenario, large applicability limitations and poor functional scalability.

[0006] The present invention proposes an accidental injury monitoring method, which comprises:

[0007] Collect the user's biometric data and current environment data;

[0008] Calling a preset baseline model and / or an unexpected scenario model, and taking data in the biometric data and / or the environmental data that meets preset conditions as associated information;

[0009] Analyze the associated information to determine the user's accidental injury scenario and accidental injury level;

[0010] Generate an alarm event corresponding to the accidental injury scenario, and provide treatment suggestions corresponding to the accidental injury level.

[0011] Optionally, the collecting of the user's biometric data and current environment data specifically includes:

[0012] Access the user's smart device and current network;

[0013] The biometric data collected and sent by the smart device is received, and the environment data related to the scene is collected from the network.

[0014] Optionally, before calling the preset baseline model and / or unexpected scenario model, the method includes:

[0015] Collect biometric data of users in normal situations, as well as network case data of unexpected events;

[0016] The baseline model of the user when not injured is established based on the biometric data under normal circumstances, and the accident scenario model related to the injury characteristics is established based on the network case data belonging to the accident event.

[0017] Optionally, taking the data in the biometric data and / or the environmental data that meets a preset condition as the associated information specifically includes:

[0018] Presetting a data threshold corresponding to the baseline model and a model feature corresponding to the unexpected scenario model;

[0019] Compare the biometric data with the baseline model, and record the data in the biometric data that exceeds the data threshold as the associated information that meets the preset conditions, and / or compare the environmental data with the unexpected scenario model, and record the data in the environmental data that meets the model characteristics as the associated information that meets the preset conditions.

[0020] Optionally, analyzing the associated information to determine the accidental injury scenario and accidental injury level of the user specifically includes:

[0021] Determine the user's accidental injury state according to the associated information, and determine the accidental injury scenario corresponding to the accidental injury state;

[0022] In the accidental injury scenario, the accidental injury level corresponding to the accidental injury state is determined based on the biometric data and / or the environmental data.

[0023] Optionally, generating an alarm event corresponding to the accidental injury scenario and providing treatment suggestions corresponding to the accidental injury level specifically includes:

[0024] Presetting an event configuration corresponding to the accident scenario model;

[0025] According to the event configuration and the baseline model, a treatment suggestion corresponding to the accidental injury level is generated.

[0026] Optionally, the biometric data includes one or more of body fat data, trace element data, body temperature data, heart rate data, blood oxygen data, posture data, sound data, breathing rate data, facial expression data and muscle tension data;

[0027] The environmental data includes one or more of location data, weather data, key event data, public notice data, schedule data, and human health data.

[0028] The present invention also proposes an accidental injury monitoring system, which includes intelligent hardware, a baseline model and configuration module, and a data processing module. The data processing module includes a data acquisition unit, a data processing unit, a judgment and decision unit, and an event processing unit, wherein:

[0029] The data collection unit is used to collect the user's biometric data and current environment data;

[0030] The data processing unit is used to call a preset baseline model and / or an unexpected scenario model, and use data in the biometric data and / or the environmental data that meets preset conditions as associated information;

[0031] The judgment and decision-making unit is used to analyze the associated information and determine the accidental injury scenario and accidental injury level of the user;

[0032] The event processing unit is used to generate an alarm event corresponding to the accidental injury scenario and provide processing suggestions corresponding to the accidental injury level.

[0033] The present invention also proposes an accidental injury monitoring device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the accidental injury monitoring method as described in any one of the above items are implemented.

[0034] The present invention also proposes a computer-readable storage medium, on which an accidental injury monitoring program is stored. When the accidental injury monitoring program is executed by a processor, the steps of the accidental injury monitoring method as described in any of the above items are implemented.

[0035] The accidental injury monitoring method, device and computer-readable storage medium of the present invention are implemented by collecting the user's biometric data and current environmental data; calling the preset baseline model and / or accidental scenario model, and taking the data in the biometric data and / or the environmental data that meets the preset conditions as the associated information; analyzing the associated information to determine the user's accidental injury scenario and accidental injury level; generating an alarm event corresponding to the accidental injury scenario, and providing processing suggestions corresponding to the accidental injury level. A more versatile and accurate accidental injury monitoring solution is implemented, which enriches the monitoring scenarios, improves the wide applicability and functional extensibility, and enhances the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0037] Figure 1is a first flow chart of the accidental injury monitoring method of the present invention;

[0038] Figure 2 is a second flow chart of the accidental injury monitoring method of the present invention;

[0039] Figure 3 is a third flow chart of the accidental injury monitoring method of the present invention;

[0040] Figure 4 is a fourth flow chart of the accidental injury monitoring method of the present invention;

[0041] Figure 5 is a fifth flow chart of the accidental injury monitoring method of the present invention;

[0042] Figure 6 is the sixth flow chart of the accidental injury monitoring method of the present invention;

[0043] Figure 7 It is a module block diagram of the accidental injury monitoring system of the present invention. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0045] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0046] Figure 1 This is the first flow chart of the accidental injury monitoring method of the present invention. This embodiment proposes an accidental injury monitoring method, which includes:

[0047] S1, collect user's biometric data and current environment data;

[0048] S2, calling a preset baseline model and / or an unexpected scenario model, and taking data in the biometric data and / or the environmental data that meets preset conditions as associated information;

[0049] S3, analyzing the associated information to determine the user's accidental injury scenario and accidental injury level;

[0050] S4. Generate an alarm event corresponding to the accidental injury scenario, and provide processing suggestions corresponding to the accidental injury level.

[0051] In this embodiment, in order to solve the current problems of single accident injury detection and monitoring scenarios, large applicability limitations, and poor functional scalability, this embodiment provides an adaptive personal accident injury monitoring solution based on the feature data provided by multiple smart devices and preset data models, and combined with relevant machine learning algorithms, personal accident injury monitoring is more accurate, timely, and easier to expand.

[0052] In this embodiment, characteristic indicators of the user are collected in real time using one or more of the user's own wearable device, the user's environment intelligent monitoring device, and the user's mobile device as the biometric data of this embodiment.

[0053] In this embodiment, one or more of the user's own wearable device, the user's environment intelligent monitoring device, and the user's mobile device are used to access the preset network in real time, so as to collect environmental data of the user's current environment.

[0054] In this embodiment, under normal circumstances, the user's biometric data is collected to establish a personal baseline model, which is used to describe the range of changes in the user's physiological characteristics when the user is not injured.

[0055] In this embodiment, under abnormal circumstances, for example, based on network cases, special accident scenario models are trained, such as indoor screaming, outdoor explosion and other scenarios, to establish an accident scenario model, which is used to describe the range of changes in environmental data when the user may be injured.

[0056] In this embodiment, the biometric data is continuously monitored and compared with the baseline model, and the corresponding associated information is output, and / or the corresponding associated information is output according to the received scene signal that triggers the unexpected scene model.

[0057] In this embodiment, based on the above-mentioned associated information, a machine learning algorithm is used for analysis to determine whether the user has suffered an accidental injury; after determining that the user has suffered an accidental injury, corresponding injury ratings are performed according to different injury scenarios.

[0058] In this embodiment, if the above analysis or judgment confirms that the user is at risk of an accident, an alarm mechanism is triggered. For example, according to the above injury classification, a corresponding processing mechanism is selected or corresponding processing suggestions are provided.

[0059] The beneficial effects of this embodiment are that by collecting the user's biometric data and current environmental data; calling a preset baseline model and / or accident scenario model, the data in the biometric data and / or the environmental data that meets the preset conditions is used as associated information; analyzing the associated information to determine the user's accidental injury scenario and accidental injury level; generating an alarm event corresponding to the accidental injury scenario, and providing processing suggestions corresponding to the accidental injury level. The embodiment of the present application realizes an accidental injury monitoring solution with greater versatility and higher accuracy, enriches the monitoring scenarios, improves the wide applicability and functional extensibility, and enhances the user experience.

[0060] Figure 2 : is a second flow chart of the accidental injury monitoring method of the present invention. Based on the above embodiment, the collection of the user's biometric data and current environmental data specifically includes:

[0061] S11, access the user's smart device and current network;

[0062] S12: receiving the biometric data collected and sent by the smart device, and collecting the environment data related to the scene from the network.

[0063] In this embodiment, the smart device includes wearable devices and other types of mobile devices, wherein the wearable devices include smart bracelets, smart watches, smart headphones, smart rings and other devices, and other types of mobile devices include mobile phones, tablet computers, smart home appliances, smart cars, smart cameras and other devices. For example, through the wearing position of the above-mentioned wearable device, the corresponding first biometric data related to the contact characteristic is obtained, and the first biometric data includes heart rate data, blood oxygen data, respiratory rate data, muscle tension data, etc.; for another example, through the above-mentioned other types of mobile devices, the corresponding second biometric data related to the non-contact characteristic is obtained, and the second biometric data includes posture data, sound data, and facial expression data, etc.

[0064] In this embodiment, when the above-mentioned biometric data is obtained through multiple smart devices, one of the devices is selected as the main device, and the biometric data collected and sent by other devices is received through the main device, and then the main device performs subsequent data processing on the received biometric data. The above-mentioned biometric data includes one or more of body fat data, trace element data, body temperature data, heart rate data, blood oxygen data, posture data, sound data, respiratory rate data, facial expression data and muscle tension data.

[0065] In this embodiment, the environmental data is obtained through at least two of the multiple smart devices, or one of the devices is selected as the main device, and the environmental data is obtained through the main device. The environmental data includes one or more of location data, weather data, key event data, announcement data, schedule data, and human health data.

[0066] In this embodiment, among multiple smart devices, the environmental data that can be obtained by each device is determined, and the environmental data is obtained by a smart device with at least one environmental data acquisition sensor. The environmental data that cannot be obtained by the smart device is collected by a smart device with data networking function.

[0067] In this embodiment, mutually related data is determined in the biometric data and the environmental data, thereby determining a device for acquiring the above two types of data. For example, it is determined that body temperature data and weather data are mutually related temperature data. In this case, the above body temperature data and weather data can be acquired through a single portable device such as a smart watch.

[0068] In this embodiment, data from various wearable devices are analyzed in combination, not limited to a specific single terminal device; standard protocols, such as MQTT (Message Queuing Telemetry Transport), are used to efficiently interact with the terminal, making terminal access more convenient.

[0069] Figure 3 3 is a flow chart of the accident injury monitoring method of the present invention. Based on the above embodiment, before calling the preset baseline model and / or accident scenario model, the method includes:

[0070] S13, collect biometric data of users in normal situations, and network case data of unexpected events;

[0071] S14. Establishing the baseline model of the user when the user is not injured based on the biometric data under normal circumstances, and establishing the accident scenario model related to the injury characteristics based on the network case data belonging to the accident event.

[0072] In this embodiment, under normal circumstances, the user's biometric data is collected to establish a personal baseline model, which is used to describe the range of changes in the user's physiological characteristics when the user is not injured.

[0073] In this embodiment, special accident scenario models are trained based on network cases. For example, a sudden scream caused by a collision is usually accompanied by the sound of a collision, the concerned expressions of the surrounding people, and related discussion words, etc.; for another example, an explosion is usually accompanied by the sound of an explosion, a commotion among people, and the sound of an alarm, etc.; by training scenario models with various characteristics, key features are extracted.

[0074] Figure 4 is a fourth flow chart of the accidental injury monitoring method of the present invention. Based on the above embodiment, the data that meets the preset conditions in the biometric data and / or the environmental data is used as the associated information, specifically including:

[0075] S21, presetting a data threshold corresponding to the baseline model and a model feature corresponding to the accidental scenario model;

[0076] S22, comparing the biometric data with the baseline model, and recording the data in the biometric data that exceeds the data threshold as the associated information that meets the preset conditions, and / or comparing the environmental data with the unexpected scenario model, and recording the data in the environmental data that meets the model characteristics as the associated information that meets the preset conditions.

[0077] In this embodiment, biometric data is continuously monitored and compared with a baseline model to determine the output associated information; for example, relevant thresholds are set in advance, and when a certain biometric exceeds a normal range (heart rate rises or falls sharply, breathing rate increases suddenly, etc.), it is recorded as corresponding associated information; wherein the associated information includes information such as time, location, and heart rate.

[0078] In this embodiment, environmental data is continuously monitored and compared with the accident scene model to determine the output related information; for example, when features that match the trained accident scene model appear, they are also recorded as related information.

[0079] In this embodiment, a data model is used to train specific scenarios, wherein pre-training is performed on a server and continuous self-learning and improvement is performed; a single terminal communicates with the server via a network using a standard communication protocol, the model is stored on the server, and the configuration parameters are stored on the terminal device, thereby solving the problem of scenario limitations of the single terminal due to physical or system resource limitations, thereby improving the accuracy of judgment. Figure 5 is a fifth flow chart of the accidental injury monitoring method of the present invention. Based on the above embodiment, the analysis of the associated information to determine the accidental injury scenario and accidental injury level of the user specifically includes:

[0080] S31, determining the user's accidental injury state according to the associated information, and determining the accidental injury scene corresponding to the accidental injury state;

[0081] S32. In the accidental injury scenario, determine the accidental injury level corresponding to the accidental injury state according to the biometric data and / or the environmental data.

[0082] In this embodiment, the collected data is analyzed using a machine learning algorithm to determine whether an accidental injury has occurred; further, the scene is judged, for example, whether it is a fall or a fall into water, and then the accidental injury is graded according to the scene. Among them, the accident level is generally divided into mild: suggestions or prompts are required, moderate: interaction is required for confirmation, and corresponding interaction instructions are provided, and help is provided according to user instructions, and severe and above: alarms and other emergency external rescue are required.

[0083] In this embodiment, after determining the accidental injury scenario corresponding to the accidental injury state, the first way to determine the accidental injury level is to determine the accidental injury level based on the biometric data. Specifically, first, the corresponding biometric data type is determined based on the accidental injury type in the accidental injury state, and then the corresponding accidental injury level is determined based on the data indicators of the type. For example, when the accidental injury is a fall state, first, the biometric data type is determined to be a muscle tension data type, and then the growth amplitude of the tension data is determined, and the corresponding accidental injury level is determined based on the amplitude.

[0084] In this embodiment, after determining the accidental injury scenario corresponding to the accidental injury state, the second way to determine the accidental injury level is to determine the accidental injury level based on environmental data. Specifically, first, the corresponding environmental characteristic data type is determined based on the environmental data in the accidental injury state, and then the corresponding accidental injury level is determined based on the data indicators of this type. For example, when the accidental injury is a fall state, first, the environmental data type is determined to be a location data type, and then the location attribute of the location is determined, and the corresponding accidental injury level is determined based on the location. For example, for an accidental injury that is a fall state, the level of the location in the public road area is higher than the level of the location in the nursing center area.

[0085] In this embodiment, after determining the accidental injury scenario corresponding to the accidental injury state, the second way to determine the accidental injury level is to jointly determine the accidental injury level based on the biometric data and the environmental data. Specifically, based on the above-mentioned type of determination, the weights of the biometric data and the environmental data are further increased, so that the accidental injury level is jointly determined based on the type and weight of the biometric data and the environmental data.

[0086] In this embodiment, the basis for judgment is usually sudden abnormal data indicators, such as sudden fluctuations in physiological data, rapid changes in movement status (i.e., from stillness to rapid movement, or then stopping); further, a comprehensive judgment is made in combination with the coordinated changes in multiple physiological data.

[0087] For example, at 8:30 p.m. on a sunny day, the monitored person's heart rate rises rapidly, and the monitored person moves rapidly in the residential area. According to the information collected by the monitored person, it is known that the monitored person has formulated a physical recovery and improvement plan, and it can be judged that the monitored person is training according to the plan. The monitored person can continue to monitor his biological indicators and provide scientific exercise reminders without the need to call the police. For another example, at 16:00 p.m. on a sunny day, the monitored person suddenly screams. According to the acceleration sensor of the watch, it is judged that there is a sudden acceleration, the heart rate rises sharply, the breathing is rapid and irregular, and the body temperature drops rapidly. The water immersion sensor of the watch reports the immersion event. According to the location display, the monitored person is in the mountains and forests, where there are waterfalls and pools, and his regular contact is 50 meters away from him; therefore, it can be judged that the monitored person accidentally fell into the water while climbing the mountain to watch the waterfall, and automatically called and sent a message to his regular contact nearby for help.

[0088] Figure 6 This is the sixth flow chart of the accidental injury monitoring method of the present invention. Based on the above embodiment, the generation of an alarm event corresponding to the accidental injury scenario and the provision of a treatment suggestion corresponding to the accidental injury level specifically include:

[0089] S41, presetting an event configuration corresponding to the accident scenario model;

[0090] S42: Generate a treatment suggestion corresponding to the accidental injury level according to the event configuration and the baseline model.

[0091] In this embodiment, if the existence of an unexpected risk is confirmed through analysis and judgment, an alarm mechanism is triggered; further, a corresponding processing mechanism is selected according to the corresponding level of the unexpected risk.

[0092] In this embodiment, one processing method is, after confirming the existence of an accident risk through analysis and judgment, first, according to the corresponding level of the accident risk, a corresponding alarm mechanism is determined, for example, the alarm handling party and the alarm reporting channel are determined, etc., and then, according to the corresponding level of the accident risk, a corresponding processing mechanism is determined, for example, self-processing, rescue processing, and waiting processing, etc.

[0093] In this embodiment, another processing method is, after confirming the existence of an accident risk through analysis and judgment, first, according to the type of the corresponding accident risk, the corresponding alarm mechanism is determined, for example, the alarm handling party and the alarm reporting channel are determined, etc., and then, according to the level of the corresponding accident risk, the corresponding processing mechanism is determined, for example, self-processing, rescue processing and waiting processing are determined.

[0094] In this embodiment, for low risk, relevant scenario suggestions are made; for medium risk, interactive confirmation is performed; for high risk, alarm processing is directly performed.

[0095] Based on the above method, in a specific example, for low-risk types of falls, while reporting to the community health service center, it is determined to handle it by oneself, and for high-risk types of falling into water, while medical emergency alarms are issued, it is determined to provide rescue to the rescue team.

[0096] In this embodiment, during the accidental injury monitoring process, flexible additions and changes are made according to the scenarios, conditions and processing methods. For example, accidental falling into water scenarios, conditions and processing methods are added, including screaming, water sounds, sudden acceleration, sudden increase in heart rate, rapid breathing, sudden drop in body temperature, etc. For another example, for water flooding incidents, the processing methods will also be linked, and the processing methods include calling for help from emergency contacts. If there is no improvement within 20 seconds, measures such as reporting to the police and calling for help from the rescue team are taken.

[0097] In this embodiment, based on physical sign data plus artificial intelligence scene judgment, accurate event judgment is performed and the most reasonable decision is made. Among them, hardware such as smart devices are used to collect physical media of various biological parameters; and the above-mentioned model is used to train and optimize the baseline model, as well as to add various unexpected scenes and configure event processing corresponding to the scene; based on this, data is collected, analyzed, and decided, and event processing after decision-making is performed according to the baseline model and configuration strategy. Therefore, with the continuous improvement of the development of smart devices and data models, on the one hand, the event processing method is more customized, and on the other hand, the judgment results are more accurate, and the decisions made are more timely and effective; and, through the combination of physical sign data and artificial intelligence scenes, accurate event judgment is performed and the most reasonable decision is made.

[0098] Based on the above embodiments, the present invention further proposes an accidental injury monitoring system, which includes intelligent hardware 100, a baseline model and configuration module 200, and a data processing module 300. The data processing module 300 includes a data acquisition unit 310, a data processing unit 320, a judgment and decision unit 330, and an event processing unit 340, wherein:

[0099] The data collection unit 310 is used to collect the user's biometric data and current environment data;

[0100] The data processing unit 320 is used to call a preset baseline model and / or an unexpected scenario model, and use the data in the biometric data and / or the environmental data that meets the preset conditions as associated information; wherein the data processing unit 320 performs comprehensive data operations and normalization, and transfers data between modules according to the data format defined by the judgment and decision unit 330, rather than directly transferring the data, for example, transferring the percentage of exceeding the threshold value and the matching degree of the unexpected scenario. The judgment and decision unit 330 uses this as an important basis for making injury level decisions.

[0101] The judgment and decision unit 330 is used to analyze the associated information to determine the accidental injury scenario and accidental injury level of the user;

[0102] The event processing unit 340 is used to generate an alarm event corresponding to the accidental injury scenario and provide processing suggestions corresponding to the accidental injury level.

[0103] It should be noted that the above-mentioned system embodiment and method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the system embodiment, which will not be repeated here.

[0104] Based on the above embodiments, the present invention also proposes an accidental injury monitoring device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the accidental injury monitoring method as described in any one of the above items are implemented.

[0105] It should be noted that the above-mentioned device embodiment and method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the device embodiment, which will not be repeated here.

[0106] Based on the above embodiments, the present invention also proposes a computer-readable storage medium, which stores an accidental injury monitoring program. When the accidental injury monitoring program is executed by a processor, the steps of the accidental injury monitoring method as described in any of the above items are implemented.

[0107] It should be noted that the above-mentioned medium embodiment and method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the medium embodiment, which will not be repeated here.

[0108] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0110] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for monitoring accidental injuries, characterized in that: The method comprises: Collect the user's biometric data and current environment data; Calling a preset baseline model and / or an unexpected scenario model, and taking data in the biometric data and / or the environmental data that meets preset conditions as associated information; Analyze the associated information to determine the user's accidental injury scenario and accidental injury level; Generate an alarm event corresponding to the accidental injury scenario, and provide treatment suggestions corresponding to the accidental injury level.

2. The accidental injury monitoring method according to claim 1, characterized in that: The collecting of the user's biometric data and current environment data specifically includes: Access the user's smart device and current network; The biometric data collected and sent by the smart device is received, and the environment data related to the scene is collected from the network.

3. The accidental injury monitoring method according to claim 1, characterized in that: Before calling the preset baseline model and / or unexpected scenario model, the method includes: Collect biometric data of users in normal situations, as well as network case data of unexpected events; The baseline model of the user when not injured is established based on the biometric data under normal circumstances, and the accident scenario model related to the injury characteristics is established based on the network case data belonging to the accident event.

4. The accidental injury monitoring method according to claim 1, characterized in that: The taking the data in the biometric data and / or the environmental data that meets the preset conditions as the associated information specifically includes: Presetting a data threshold corresponding to the baseline model and a model feature corresponding to the unexpected scenario model; Compare the biometric data with the baseline model, and record the data in the biometric data that exceeds the data threshold as the associated information that meets the preset conditions, and / or compare the environmental data with the unexpected scenario model, and record the data in the environmental data that meets the model characteristics as the associated information that meets the preset conditions.

5. The accidental injury monitoring method according to claim 1, characterized in that: The analyzing the associated information to determine the accidental injury scenario and accidental injury level of the user specifically includes: Determine the user's accidental injury state according to the associated information, and determine the accidental injury scenario corresponding to the accidental injury state; In the accidental injury scenario, the accidental injury level corresponding to the accidental injury state is determined based on the biometric data and / or the environmental data.

6. The accidental injury monitoring method according to claim 1, characterized in that: The generating of an alarm event corresponding to the accidental injury scenario and providing treatment suggestions corresponding to the accidental injury level specifically includes: Presetting an event configuration corresponding to the accident scenario model; According to the event configuration and the baseline model, a treatment suggestion corresponding to the accidental injury level is generated.

7. The accidental injury monitoring method according to claim 2, characterized in that: The biometric data includes one or more of body fat data, trace element data, body temperature data, heart rate data, blood oxygen data, posture data, sound data, breathing rate data, facial expression data, and muscle tension data; The environmental data includes one or more of location data, weather data, key event data, public notice data, schedule data, and human health data.

8. An accidental injury monitoring system, characterized in that: The system includes intelligent hardware, a baseline model and configuration module, and a data processing module. The data processing module includes a data acquisition unit, a data processing unit, a judgment and decision unit, and an event processing unit, wherein: The data collection unit is used to collect the user's biometric data and current environment data; The data processing unit is used to call a preset baseline model and / or an unexpected scenario model, and use data in the biometric data and / or the environmental data that meets preset conditions as associated information; The judgment and decision-making unit is used to analyze the associated information to determine the accidental injury scenario and accidental injury level of the user; The event processing unit is used to generate an alarm event corresponding to the accidental injury scenario and provide processing suggestions corresponding to the accidental injury level.

9. An accidental injury monitoring device, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the accidental injury monitoring method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an accidental injury monitoring program, and when the accidental injury monitoring program is executed by the processor, the steps of the accidental injury monitoring method as described in any one of claims 1 to 7 are implemented.

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